U.S. patent application number 10/793419 was filed with the patent office on 2004-09-23 for pulse meter, method for controlling pulse meter, wristwatch-type information device, control program, storage medium, blood vessel simulation sensor, and living organism information measurement device.
This patent application is currently assigned to Seiko Epson Corporation. Invention is credited to Aoshima, Ichiro, Baba, Norimitsu, Kawafune, Yutaka, Kosuda, Tsukasa, Zakoji, Makoto.
Application Number | 20040186387 10/793419 |
Document ID | / |
Family ID | 32995607 |
Filed Date | 2004-09-23 |
United States Patent
Application |
20040186387 |
Kind Code |
A1 |
Kosuda, Tsukasa ; et
al. |
September 23, 2004 |
Pulse meter, method for controlling pulse meter, wristwatch-type
information device, control program, storage medium, blood vessel
simulation sensor, and living organism information measurement
device
Abstract
The present invention realizes calculating a pulse rate
accurately, even when a body movement component has no periodical
characteristics, by surely removing the body movement component
generated in a living organism from a pulse wave component. A pulse
wave detecting section includes a pulse wave sensor and outputs a
pulse wave detection signal to an MPU functioning as a body motion
component removing section. A body motion sensor outputs a body
motion detection signal corresponding to a body motion that affects
the behavior of venous blood to the MPU. As a result, to the MPU
removes the body motion component from the pulse wave detection
signal based on the body motion detection signal. A pulse rate
calculating section calculates the pulse rate based on the pulse
wave detection signal from which the body motion component has been
removed. The pulse rate is displayed on a liquid crystal display
device.
Inventors: |
Kosuda, Tsukasa;
(Matsumoto-shi, JP) ; Zakoji, Makoto;
(Shiojiri-shi, JP) ; Aoshima, Ichiro; (Nagano-ken,
JP) ; Kawafune, Yutaka; (Matsumoto-shi, JP) ;
Baba, Norimitsu; (Shiojiri-shi, JP) |
Correspondence
Address: |
SHINJYU GLOBAL IP COUNSELORS, LLP
1233 20TH STREET, NW, SUITE 700
WASHINGTON
DC
20036-2680
US
|
Assignee: |
Seiko Epson Corporation
Tokyo
JP
|
Family ID: |
32995607 |
Appl. No.: |
10/793419 |
Filed: |
March 5, 2004 |
Current U.S.
Class: |
600/502 |
Current CPC
Class: |
A61B 5/681 20130101;
A61B 2562/0219 20130101; A61B 5/721 20130101; A61B 5/11 20130101;
A61B 5/7257 20130101; A61B 5/02 20130101 |
Class at
Publication: |
600/502 |
International
Class: |
A61B 005/02 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 19, 2003 |
JP |
2003-075839 |
Mar 19, 2003 |
JP |
2003-075840 |
Sep 2, 2003 |
JP |
2003-310624 |
Claims
What is claimed is:
1. A living organism information measurement device adapted to be
attached to a human body to measure living organism information,
comprising: a pulse wave detecting section configured and arranged
to output a pulse wave detection signal by using a pulse wave
sensor; a body motion component removing section configured and
arranged to detect a body motion component resulting from venous
blood movements of the human body that is contained in said pulse
wave detection signal and remove said body motion component
contained in said pulse wave detection signal; and a living
organism information measuring section configured and arranged to
measure living organism information based on said pulse wave
detection signal from which said body motion component has been
removed.
2. A pulse meter adapted to be attached to a human body to measure
a pulse of the human body, comprising: a pulse wave detecting
section configured and arranged to output a pulse wave detection
signal by using a pulse wave sensor; a body motion detecting
section configured and arranged to detect accelerations
corresponding to body motions that affect a venous blood behavior
by using an acceleration sensor and output a body motion detection
signal; a body motion component removing section configured and
arranged to remove a body motion component contained in said pulse
wave detection signal based on said body motion detection signal;
and a pulse rate calculating section configured and arranged to
calculate a pulse rate based on said pulse wave detection signal
from which said body motion component has been removed.
3. The pulse meter as recited in claim 2, wherein said acceleration
sensor is a triaxial acceleration sensor that is configured and
arranged to detect accelerations in directions along an X-axis, a
Y-axis and a Z-axis, said X-axis being defined as an axis
substantially parallel to an arm of a user, said Z-axis being
defined as an axis that is perpendicular to said X-axis and a palm
of the user, and said Y-axis being defined as an axis that is
perpendicular to said X-axis and said Y-axis.
4. The pulse meter as recited in claim 2, wherein said acceleration
sensor is positioned in the vicinity of said pulse wave sensor.
5. The pulse meter as recited in claim 2, wherein said acceleration
sensor is substantially stacked on said pulse wave sensor.
6. The pulse meter as recited in claim 3, wherein said body motion
component removing section includes a body motion component
generating section that is configured and arranged to generate said
body motion component based on said X-axis acceleration component,
said Y-axis acceleration component, and said Z-axis acceleration
component.
7. The pulse meter as recited in claim 6, wherein said body motion
component generating section is configured and arranged to generate
said body motion component based on said X-axis acceleration
component and a biaxial integrated acceleration component obtained
by integrating vectors corresponding to said Y-axis acceleration
component and said Z-axis acceleration component.
8. The pulse meter as recited in claim 6, wherein said body motion
component generating section is configured and arranged to generate
said body motion component based on a triaxial integrated
acceleration component obtained by integrating vectors
corresponding to said X-axis acceleration component, said Y-axis
acceleration component and said Z-axis acceleration component.
9. The pulse meter as recited in claim 6, wherein at least one of
said X-axis acceleration component, said Y-axis acceleration
component and said Z-axis acceleration component is weighted in
said body motion component generating section.
10. The pulse meter as recited in claim 6, wherein said body motion
component removing section includes a filter coefficient generating
section configured and arranged to generate an adaptive filter
coefficient based on said X-axis acceleration component, said
Y-axis acceleration component and said Z-axis acceleration
component, and a removal processing section configured and arranged
to remove said body motion component from said pulse wave detection
signal based on said adaptive filter coefficient.
11. The pulse meter as recited in claim 6, wherein said body motion
component removing section is configured and arranged to remove a
prescribed low frequency band component contained as the body
motion component from said pulse wave detection signal by using a
prescribed simulated low-frequency signal.
12. The pulse meter as recited in claim 10, wherein said filter
coefficient generating section is configured and arranged to
generate an adaptive filter coefficient based on a prescribed
simulated low-frequency component to remove a prescribed low
frequency band component contained as the body motion component
from said pulse wave detection signal by using said prescribed
simulated low-frequency signal, and said removal processing section
is configured and arranged to remove the body motion component from
said pulse wave detection signal based on said adaptive filter
coefficient.
13. The pulse meter as recited in claim 2, comprising a body motion
information detecting section configured and arranged to detect a
pitch of step or step counts from said body motion component
contained in said pulse wave detection signal.
14. A method for measuring a pulse of a human body, comprising:
performing pulse wave detection signal outputting process for
outputting a pulse wave detection signal detected by a pulse wave
sensor attached on the human body; performing acceleration
detecting process for detecting accelerations corresponding to body
motions that affect a venous blood behavior using an acceleration
sensor attached on the human body, said acceleration sensor being a
triaxial acceleration sensor configured and arranged to detect
accelerations in directions along an X-axis, a Y-axis and a Z-axis,
said X-axis being defined as an axis substantially parallel to an
arm of a user, said Z-axis being defined as an axis that is
perpendicular to said X-axis and a palm of the user, and said
Y-axis is defined as an axis that is perpendicular to said X-axis
and said Y-axis; performing body motion detection signal outputting
process for outputting a body motion detection signal corresponding
to said accelerations detected in said acceleration detecting
process; performing body motion component generating process for
generating a body motion component based on an X-axis acceleration
component in a direction along said X-axis, a Y-axis acceleration
component in a direction along said Y-axis, and a Z-axis
acceleration component in a direction along said Z-axis; performing
body motion component removing process for removing said body
motion component from said pulse wave detection signal; and
performing pulse rate calculating process for calculating a pulse
rate based on said pulse wave detection signal from which said body
motion component has been removed.
15. The method as recited in claim 14, wherein said body motion
component generating process includes generating said body motion
component based on said X-axis acceleration component and a biaxial
integrated acceleration component obtained by integrating vectors
corresponding to said Y-axis acceleration component and said Z-axis
acceleration component.
16. The method as recited in claim 14, wherein said body motion
component generating process includes generating said body motion
component based on a triaxial integrated acceleration component
obtained by integrating vectors corresponding to said X-axis
acceleration component, said Y-axis acceleration component and said
Z-axis acceleration component.
17. The method as recited in claim 14, wherein said body motion
component removing process includes removing a prescribed low
frequency band component contained as said body motion component
from said pulse wave detection signal by using a prescribed
simulated low-frequency signal.
18. A wristwatch type information device configured to be placed on
an arm of a user, comprising: a pulse wave detecting section
configured and arranged to output a pulse wave detection signal by
using a pulse wave sensor; a body motion component generating
section configured and arranged to generate a body motion component
based on an X-axis acceleration component in a direction along an
X-axis, a Y-axis acceleration component in a direction along a
Y-axis and a Z-axis acceleration component in a direction along a
Z-axis detected by a triaxial acceleration sensor, said X-axis
being defined as an axis substantially parallel to the arm of the
user, said Z-axis being defined as an axis that is perpendicular to
said X-axis and a palm of the user, and said Y-axis being defined
as an axis that is perpendicular to said X-axis and said Y-axis; a
body motion component removing section configured and arranged to
remove said body motion component from said pulse wave detection
signal; a pulse rate calculating section configured and arranged to
calculate a pulse rate based on said pulse wave detection signal
after said body motion component is removed; and a display section
configured and arranged to display said pulse rate.
19. A control program for controlling, by a computer, a pulse meter
adapted to be attached to a human body to measure a pulse of the
human body and having a pulse wave detecting section configured and
arranged to output a pulse wave detection signal by using a pulse
wave sensor and a body motion detecting section configured and
arranged to detect accelerations corresponding to body motions that
affect a venous blood behavior by using an acceleration sensor and
output a body motion detection signal, said control program
comprising instructions for performing: removing a body motion
component contained in said pulse wave detection signal based on
said body motion detection signal; and calculating a pulse rate
based on said pulse wave detection signal after said body motion
component is removed.
20. A pulse meter adapted to be attached to a human body to measure
a pulse, comprising: a pulse wave detecting section configured and
arranged to output a pulse wave detection signal by using a pulse
wave sensor; a body motion component removing section configured
and arranged to remove a body motion component contained in said
pulse wave detection signal based on a relative positional
difference in a vertical direction between a position of the heart
of the human body and a position where said pulse meter is
attached; and a pulse rate calculating section configured and
arranged to calculate a pulse rate based on said pulse wave
detection signal after said body motion component is removed.
21. The pulse meter as recited in claim 20, wherein said body
motion component removing section includes a body motion detecting
section configured and arranged to detect a body motion component
expressed as a function of said relative positional difference and
output a body motion detection signal.
22. The pulse meter as recited in claim 21, wherein said body
motion detecting section includes a pressure sensor configured and
arranged to detect said body motion component.
23. The pulse meter as recited in claim 22, wherein said pressure
sensor is positioned in the vicinity of said pulse wave sensor.
24. The pulse meter as recited in claim 22, wherein said pressure
sensor is substantially stacked on said pulse wave sensor.
25. The pulse meter as recited in claim 24, wherein said body
motion component removing section includes a difference detecting
section configured and arranged to detect said relative positional
difference, and a body motion component generating section
configured and arranged to generate said body motion component
based on said relative positional difference.
26. The pulse meter as recited in claim 25, wherein said difference
detecting section includes an angle sensor configured and arranged
to detect, as said relative positional difference, an angle
difference of an actual position of said pulse meter with respect
to a reference angle of said pulse meter.
27. The pulse meter as recited in claim 26, wherein said angle
sensor is positioned in the vicinity of said pulse wave sensor.
28. The pulse meter as recited in claim 26, wherein said angle
sensor is substantially stacked on said pulse wave sensor.
29. The pulse meter as recited in claim 26, wherein said angle
sensor is configured and arranged to detect said angle difference
based on a stationary acceleration.
30. The pulse meter as recited in claim 26, wherein said angle
sensor is configured and arranged to have a rotary spindle and
detect said angle difference based on a rotational state of said
rotary spindle.
31. The pulse meter as recited in claim 25, wherein said difference
detecting section includes an angle compensating section configured
and arranged to compensate said angle difference according to said
prescribed body motion component when said angle difference
indicates said position where said pulse meter is attached is
higher than said position of the heart of the human body by an
amount greater than a threshold value.
32. The pulse meter as recited in claim 20, wherein said body
motion component removing section includes a removal processing
section configured and arranged to subtract a body motion detection
signal corresponding to said body motion component based on said
relative positional difference from said pulse wave detection
signal.
33. The pulse meter as recited in claim 20, wherein said body
motion removing section includes a first frequency analyzing
section configured and arranged to execute a frequency analysis of
a body motion component detection signal corresponding to said body
motion component based on said relative positional difference and
generate first frequency analysis data, a second frequency
analyzing section configured and arranged to execute a frequency
analysis of said pulse wave detection signal and generate second
frequency analysis data, and a removal processing section
configured and arranged to subtract said first frequency analysis
data from said second frequency analysis data.
34. The pulse meter as recited in claim 20, wherein said body
motion component removing section includes a filter coefficient
generating section configured and arranged to generate an adaptive
filter coefficient based on a body motion component detection
signal corresponding to said body motion component based on said
relative positional difference, and a removal processing section
configured and arranged to subtract said body motion component
detection signal applied with said adaptive filter coefficient from
said pulse wave detection signal.
35. The pulse meter as recited in claim 20, comprising a body
motion information detecting section configured and arranged to
detect a pitch of step or step counts from said body motion
component contained in said pulse wave detection signal based on
said relative positional difference.
36. A method for measuring a pulse of a human body, comprising:
performing pulse wave detecting process for outputting a pulse wave
signal using by a pulse wave sensor attached to the human body;
performing body motion component removing process for removing a
body motion component contained in said pulse wave detection signal
based on a relative positional difference in a vertical direction
between a position of the heart of the human body and a position
where said pulse meter is attached; and performing pulse rate
calculating process for calculating a pulse rate based on said
pulse wave detection signal after said body motion component is
removed.
37. A wristwatch type information device, comprising: a pulse wave
detecting section configured and arranged to be placed on a pulse
wave detection position of the human body to output a pulse wave
detection signal by using a pulse wave sensor; and a main body
configured and arranged to be placed on a wrist of the human body,
said main body including a body motion component removing section
configured and arranged to remove a body motion component contained
in said pulse wave detection signal based on a relative positional
difference in a vertical direction between a position of the heart
of the human body and a position where said pulse wave detecting
section is attached, a pulse rate calculating section configured
and arranged to calculate a pulse rate based on said pulse wave
detection signal after said body motion component is removed, and a
display section configured and arranged to display said pulse
rate.
38. A control program for controlling, by a computer, a pulse meter
adapted to be attached to a human body to measure a pulse and
having a pulse wave detecting section configured and arranged to
output a pulse wave signal by using a pulse wave sensor, said
control program comprising instructions for performing: removing a
body motion component contained in said pulse wave detection signal
based on a relative positional difference in a vertical direction
between a position of the heart of the human body and a position
where said pulse meter is attached; and calculating a pulse rate
based on said pulse wave detection signal after said body motion
component is removed.
39. A computer readable medium configured and arranged to store a
control program for controlling, by a computer, a pulse meter
adapted to be attached to a human body to measure a pulse of the
human body and having a pulse wave detecting section configured and
arranged to output a pulse wave detection signal by using a pulse
wave sensor and a body motion detecting section configured and
arranged to detect accelerations corresponding to body motions that
affect a venous blood behavior by using an acceleration sensor and
output a body motion detection signal, said control program
comprising instructions for performing: removing a body motion
component contained in said pulse wave detection signal based on
said body motion detection signal; and calculating a pulse rate
based on said pulse wave detection signal after said body motion
component is removed.
40. A blood vessel simulation sensor adapted to be attached to a
human body to simulate a behavior of venous blood of the human
body, comprising: a casing; simulation blood disposed inside said
casing and having a viscosity substantially equal to a viscosity of
the venous blood; and a behavior detection sensor configured and
arranged to detect a behavior of said simulation blood.
41. The blood vessel simulation sensor as recited in claim 40,
wherein said casing is made of a rigid material.
42. The blood vessel simulation sensor as recited in claim 41,
wherein said casing includes a transparent resin tube with each end
of said tube being closed, and said behavior detection sensor is a
photodetector configured and arranged to measure a change in a
surface of said simulation blood.
43. The blood vessel simulation sensor as recited in claim 41,
wherein said casing includes a resin tube with each end of said
tube being closed, and said behavior detection sensor is a pressure
sensor positioned at one end of said casing to detect a change in a
pressure as said simulation blood moves inside said casing.
44. The blood vessel simulation sensor as recited in claim 40,
wherein said casing is made of a resilient material.
45. The blood vessel simulation sensor as recited in claim 44,
wherein said casing includes a tube with each end of said tube
being closed, and said behavior detection sensor is a pressure
sensor positioned at one end of said casing to detect a change in a
pressure as said simulation blood moves inside said casing.
46. The blood vessel simulation sensor as recited in claim 44,
wherein said casing includes a tube with each end of said tube
being closed, and said behavior detection sensor is a pressure
sensor positioned on a side of said casing to detect a change in a
pressure as said simulation blood moves inside said casing.
47. A blood vessel simulation sensor adapted to be attached to a
human body to simulate a behavior of venous blood of the human
body, comprising: an acceleration sensor having a sensitivity axis
in a direction substantially toward a peripheral direction of the
human body to output a signal corresponding to a movement of the
venous blood toward said peripheral direction.
48. A pulse meter adapted to be attached to a human body to measure
a pulse of the human body, comprising: a pulse wave detecting
section configured and arranged to output a pulse wave detection
signal by using a pulse wave sensor; a blood vessel simulation
sensor configured and arranged to be attached to a human body and
simulate a behavior of venous blood of the human body, including a
casing, simulation blood disposed inside said casing and having a
viscosity substantially equal to a viscosity of the venous blood,
and a behavior detection sensor configured and arranged to detect a
behavior of said simulation blood; a body motion component removing
section configured and arranged to remove a simulation body motion
component corresponding to an output signal from said behavior
detection sensor from said pulse wave detection signal; and a pulse
rate calculating section configured and arranged to calculate a
pulse rate based on said pulse wave detection signal after said
simulation body motion component is removed.
49. The pulse meter as recited in claim 48, wherein said blood
vessel simulation sensor is positioned in the vicinity of said
pulse wave sensor.
50. The pulse meter as recited in claim 48, wherein said blood
vessel simulation sensor is substantially stacked on said pulse
wave sensor in a direction that is spaced away from the human
body.
51. The pulse meter as recited in claim 48, wherein said body
motion component removing section includes a removal processing
section configured and arranged to subtract a body motion component
detection signal corresponding to the output signal from said
behavior detection sensor from said pulse wave detection
signal.
52. The pulse meter as recited in claim 48, wherein said body
motion component removing section includes a first frequency
analyzing section configured and arranged to execute a frequency
analysis of a body motion component detection signal corresponding
to said output signal from said behavior detection sensor and
generate first frequency analysis data, a second frequency
analyzing section configured and arranged to execute a frequency
analysis of said pulse wave detection signal and generate second
frequency analysis data, and a removal processing section
configured and arranged to execute a subtraction processing of said
first frequency analysis data with respect to said second frequency
analysis data.
53. The pulse meter as recited in claim 48, wherein said body
motion component removing section includes a filter coefficient
generating section configured and arranged to generate an adaptive
filter coefficient based on a body motion detection signal
corresponding to the output signal from said behavior detection
sensor, and a removal processing section configured and arranged to
subtract said body motion detection signal to which said adaptive
filter coefficient has been applied from said pulse wave detection
signal.
54. A living organism information measurement device, comprising: a
blood vessel simulation sensor configured and arranged to be
attached to a human body and simulate a behavior of venous blood of
the human body, including a casing, simulation blood disposed
inside said casing and having a viscosity substantially equal to a
viscosity of the blood in vein, and a behavior detection sensor
configured and arranged to detect a behavior of said simulation
blood; and a living organism information detecting section
configured and arranged to detect a pitch of step or step counts
corresponding to body motions of the human body based on an output
signal of said blood vessel simulation sensor.
55. A computer readable medium configured and arranged to store a
control program for controlling, by a computer, a pulse meter
adapted to be attached to a human body to measure a pulse and
having a pulse wave detecting section configured and arranged to
output a pulse wave signal by using a pulse wave sensor, said
control program comprising instructions for performing: removing a
body motion component contained in said pulse wave detection signal
based on a relative positional difference in a vertical direction
between a position of the heart of the human body and a position
where said pulse meter is attached; and calculating a pulse rate
based on said pulse wave detection signal after said body motion
component is removed.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention relates to a pulse meter, a method for
controlling a pulse meter, a wristwatch-type information device, a
control program, a storage medium, a blood vessel simulation
sensor, and a living organism information measurement device. The
present invention particularly relates to a pulse meter, a method
for controlling a pulse meter, a wristwatch-type information
device, a control program, a storage medium, a blood vessel
simulation sensor, and a living organism information measurement
device that are suitable for being mounted on a person's arm and
measuring pulse during walking or running.
[0003] 2. Background Information
[0004] Pulse meters mounted on part of the body and designed for
measuring pulse during walking or running are conventionally
known.
[0005] For example, a wristwatch-type pulse meter is disclosed in
Japanese Patent No. 2816944. The pulse meter disclosed in this
literature employs a configuration wherein the frequency components
corresponding to all the harmonic components of a body motion
signal detected by an acceleration sensor are removed from the
frequency analysis results of a pulse wave signal based on the
frequency analysis results of the body motion signal, the frequency
components having the maximum power are extracted from among the
frequency analysis results of the pulse wave signal from which the
harmonic components of the body motion signal have been removed,
and the pulse rate is calculated based on the extracted frequency
components.
[0006] In the above-mentioned conventional pulse meter, not all the
body motion components generated in the body and included in the
pulse sensor signal are necessarily registered because the body
motion components are detected by the acceleration sensor, and it
has been possible that the removal of the body motion components
may not be complete.
[0007] In conventional practice, the body motion components cannot
be registered completely, so the body motion signal is identified
using the characteristics of the harmonic components from the
frequency analysis results in order to remove the body motion
components contained in the pulse sensor signal, and because the
identified body motion signal is removed and the pulse wave signal
extracted, there have been problems in that the body motion
components cannot be removed and, consequently, the pulse cannot be
correctly determined when the body motion does not have cyclic
characteristics.
[0008] In view of the above, it will be apparent to those skilled
in the art from this disclosure that there exists a need for an
improved pulse meter, method for controlling a pulse meter,
wristwatch-type information device, control program, storage
medium, blood vessel simulation sensor, and living organism
information measurement device. This invention addresses this need
in the art as well as other needs, which will become apparent to
those skilled in the art from this disclosure.
SUMMARY OF THE INVENTION
[0009] An object of the present invention is to provide a pulse
meter, a method for controlling a pulse meter, a wristwatch-type
information device, a control program, a storage medium, a blood
vessel simulation sensor, and a living organism information
measurement device that can accurately remove the body motion
components generated in the body from the pulse components and
calculate the pulse rate even when the body motion components do
not have cyclic characteristics by more accurately registering the
body motion components contained in the pulse sensor signal.
[0010] In order to achieve the above-mentioned and other
objectives, a living organism information measurement device
adapted to be attached to a human body to measure living organism
information is provided that comprises a pulse wave detecting
section, a body motion component removing section and a living
organism information measuring section. The pulse wave detecting
section is configured and arranged to output a pulse wave detection
signal by using a pulse wave sensor. The body motion component
removing section is configured and arranged to detect a body motion
component resulting from vein movements of the human body that is
contained in the pulse wave detection signal and remove said body
motion component contained in the pulse wave detection signal. The
living organism information measuring section is configured and
arranged to measure living organism information based on the pulse
wave detection signal from which the body motion component has been
removed.
[0011] According to another aspect of the present invention, a
pulse meter adapted to be attached to a human body to measure a
pulse of the human body is provided that comprises a pulse wave
detecting section, a body motion detecting section, a body motion
component removing section, and a pulse rate calculating section.
The pulse wave detecting section is configured and arranged to
detect a pulse wave based on a signal from a pulse wave sensor and
output a pulse wave detection signal. The body motion detecting
section is configured and arranged to detect accelerations
corresponding to body motions that affect a vein behavior based on
a signal from an acceleration sensor and output a body motion
detection signal. The body motion component removing section is
configured and arranged to remove a body motion component contained
in the pulse wave detection signal based on the body motion
detection signal. The pulse rate calculating section is configured
and arranged to calculate a pulse rate based on the pulse wave
detection signal from which the body motion component has been
removed.
[0012] According to another aspect of the present invention, a
pulse meter adapted to be attached to a human body to measure a
pulse is provided that comprises a pulse wave detecting section, a
body motion component removing section and a pulse rate calculating
section. The pulse wave detecting section is configured and
arranged to detect a pulse wave based on a signal from a pulse wave
sensor and output a pulse wave detection signal. The body motion
component removing section is configured and arranged to remove a
body motion component contained in the pulse wave detection signal
based on a relative positional difference in a vertical direction
between a position of a heart of the human body and a position
where the pulse meter is attached. The pulse rate calculating
section is configured and arranged to calculate a pulse rate based
on the pulse wave detection signal from which the body motion
component has been removed.
[0013] According to another aspect of the present invention, a
blood vessel simulation sensor adapted to be attached to a human
body to simulate a behavior of blood in vein of the human body is
provided that comprises a casing, a simulation blood and a behavior
detection sensor. The simulation blood is disposed inside the
casing and has a viscosity substantially equal to a viscosity of
the blood in vein. The behavior detection sensor is configured and
arranged to detect a behavior of the simulation blood.
[0014] These and other objects, features and advantages of the
present invention will become apparent to those skilled in the art
from the following detailed description, which, taken in
conjunction with the annexed drawings, discloses preferred
embodiments of the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Referring now to the attached drawings which form a part of
this original disclosure:
[0016] FIG. 1 is an explanatory diagram of the relationship between
the amount of change in a combined vector of acceleration vectors
along two axes and the amount of body motion components (amount of
stroke components) included in the output of a pulse sensor;
[0017] FIG. 2 is an explanatory diagram of the manner in which the
pulse measurement device of a first embodiment is mounted;
[0018] FIG. 3 is a cross-sectional view of the pulse measurement
device of the first embodiment;
[0019] FIG. 4 is a schematic structural block diagram of the pulse
measurement device of the first embodiment;
[0020] FIG. 5 is a schematic structural block diagram of an example
of an adaptive filter of the first embodiment;
[0021] FIG. 6 is a graph showing a chronological arrangement of
X-axis acceleration data Kx corresponding to an X-axis acceleration
detection signal outputted from an X-axis acceleration sensor
12X;
[0022] FIG. 7 shows the frequency analysis results obtained by
subjecting the detected X-axis acceleration data Kx in FIG. 6 to
FFT;
[0023] FIG. 8 is a graph showing a chronological arrangement of
Y-axis acceleration data Ky corresponding to a Y-axis acceleration
detection signal outputted from a Y-axis acceleration sensor
12Y;
[0024] FIG. 9 shows the frequency analysis results obtained by
subjecting the detected Y-axis acceleration data Ky in FIG. 8 to
FFT;
[0025] FIG. 10 is a graph showing a chronological arrangement of
Z-axis acceleration data Kz corresponding to a Z-axis acceleration
detection signal outputted from a Z-axis acceleration sensor
12Z;
[0026] FIG. 11 shows the frequency analysis results obtained by
subjecting the detected Z-axis acceleration data Kz in FIG. 10 to
FFT;
[0027] FIG. 12 is a graph obtained by treating the Y-axis
acceleration data Ky corresponding to the Y-axis acceleration
detection signal outputted from the Y-axis acceleration sensor 12Y,
and the Z-axis acceleration data Kz corresponding to the Z-axis
acceleration detection signal outputted from the Z-axis
acceleration sensor 12Z as vectors, and chronologically arranging
combined acceleration vector data obtained as a combined vector
thereof;
[0028] FIG. 13 shows the frequency analysis results obtained by
subjecting the combined acceleration vector data (={square
root}(Ky.sup.2+Kz.sup.2)) in FIG. 12 to FFT;
[0029] FIG. 14 is a graph showing a chronological arrangement of a
preset simulated low-frequency signal (using a triangular
wave);
[0030] FIG. 15 shows the frequency analysis results obtained by
subjecting the simulated low-frequency signal in FIG. 14 to
FFT;
[0031] FIG. 16 is a graph of a chronological arrangement of one
example of the detected pulse data;
[0032] FIG. 17 shows the frequency analysis results obtained by
subjecting the detected pulse data in FIG. 16 to FFT;
[0033] FIG. 18 is a graph plotted as a result of a chronological
arrangement of residual data obtained by combining the signals
obtained by applying an adaptive filter to the amplified X-axis
acceleration detection signal in FIG. 6, the combined acceleration
vector signal in FIG. 12, and the simulated low-frequency signal in
FIG. 14 for the pulse wave detection signal in FIG. 16;
[0034] FIG. 19 shows the frequency analysis results obtained by
subjecting the residual data in FIG. 18 to FFT;
[0035] FIG. 20 is a graph plotted as a result of a chronological
arrangement of residual data obtained by combining the signals
obtained by applying an adaptive filter to the amplified X-axis
acceleration detection signal in FIG. 6 and the combined
acceleration vector signal in FIG. 12 for the pulse wave detection
signal in FIG. 16;
[0036] FIG. 21 shows the frequency analysis results obtained by
subjecting the residual data in FIG. 20 to FFT;
[0037] FIG. 22 is a schematic structural block diagram of one
example of an adaptive filter according to a first alternative of
the first embodiment;
[0038] FIG. 23 is a graph of a chronological arrangement of
detected X-axis acceleration data Kx;
[0039] FIG. 24 shows the frequency analysis results obtained by
subjecting the detected X-axis acceleration data Kx in FIG. 23 to
FFT;
[0040] FIG. 25 is a graph of a chronological arrangement of Y-axis
acceleration data Ky;
[0041] FIG. 26 shows the frequency analysis results obtained by
subjecting the Y-axis acceleration data Ky in FIG. 25 to FFT;
[0042] FIG. 27 is a graph of a chronological arrangement of Z-axis
acceleration data Kz;
[0043] FIG. 28 shows the frequency analysis results obtained by
subjecting the Z-axis acceleration data Kz in FIG. 27 to FFT;
[0044] FIG. 29 is a graph of a chronological arrangement of
combined acceleration vector data (={square
root}(Kx.sup.2+Ky.sup.2+Kz.sup.2));
[0045] FIG. 30 shows the frequency analysis results obtained by
subjecting the combined acceleration vector data (={square
root}(Kx.sup.2+Ky.sup.2+K- z.sup.2)) to FFT;
[0046] FIG. 31 is a graph of a chronological arrangement of one
example of detected pulse wave data;
[0047] FIG. 32 shows the frequency analysis results obtained by
subjecting the detected pulse wave data in FIG. 31 to FFT;
[0048] FIG. 33 is a graph of a chronological arrangement of
residual data obtained by combining the data obtained by applying
an adaptive filter to the combined acceleration vector data in FIG.
29 and the simulated low-frequency signal in FIG. 14 for the
deteceted pulse wave data in FIG. 31;
[0049] FIG. 34 shows the frequency analysis results obtained by
subjecting the residual data in FIG. 33 to FFT;
[0050] FIG. 35 is a schematic structural block diagram of one
example of an adaptive filter according to a second alternative of
the first embodiment;
[0051] FIG. 36 is a graph of a chronological arrangement of one
example of detected pulse wave data;
[0052] FIG. 37 shows the frequency analysis results obtained by
subjecting the detected pulse wave data in FIG. 36 to FFT;
[0053] FIG. 38 is a graph of a chronological arrangement of
residual data obtained by combining the signals obtained by
applying an adaptive filter to the combined acceleration vector
signal in FIG. 29 and the simulated low-frequency signal in FIG. 14
for the detected pulse wave data in FIG. 31;
[0054] FIG. 39 shows the frequency analysis results obtained by
subjecting the residual data in FIG. 38 to FFT;
[0055] FIG. 40 is a schematic structural block diagram of one
example of an adaptive filter according to a third alternative of
the first embodiment;
[0056] FIG. 41 is a schematic structural block diagram of one
example of an adaptive filter according to a fourth alternative of
the first embodiment;
[0057] FIG. 42 is an explanatory diagram of the relationship
between the amount of change in pressure and the amount of body
motion components (amount of stroke components) included in the
pulse wave sensor output;
[0058] FIG. 43 is a schematic structural diagram of a pulse
measurement device of the second embodiment;
[0059] FIG. 44 is an explanatory diagram of the arrangement of
sensors in the sensor module of the pulse measurement device of the
second embodiment;
[0060] FIG. 45 is a schematic structural block diagram of the pulse
measurement device of the second embodiment;
[0061] FIG. 46 is a graph of a chronological arrangement of one
example of detected pulse wave data;
[0062] FIG. 47 is a graph in which detected pressure data
correlated with the detected pulse wave data in FIG. 46 is
chronologically arranged along the same time axis;
[0063] FIG. 48 is a graph of a chronological arrangement of
differential data calculated from the detected pulse wave data in
FIG. 46 and the detected pressure data in FIG. 6;
[0064] FIG. 49 shows the frequency analysis results obtained by
subjecting the differential data in FIG. 48 to FFT;
[0065] FIG. 50 is an explanatory diagram of the frequency analysis
results of the detected pulse wave data according to a first
alternative of the second embodiment;
[0066] FIG. 51 is an explanatory diagram of the frequency analysis
results of the detected pressure data according to the first
alternative of the second embodiment;
[0067] FIG. 52 is an explanatory diagram of differential data,
which is the difference between the detected pulse wave data
analyzed for frequency and the detected pressure data analyzed for
frequency, according to the first alternative of the second
embodiment;
[0068] FIG. 53 shows a schematic structural block diagram of one
example of the adaptive filter in accordance with a second
alternative of the second embodiment;
[0069] FIG. 54 is a graph of a chronological arrangement of an
example of the detected pulse wave data according to the second
alternative of the second embodiment;
[0070] FIG. 55 is a graph in which detected pressure data
correlated with the detected pulse wave data in FIG. 54 is
chronologically arranged along the same time axis;
[0071] FIG. 56 is a graph of a chronological arrangement of
residual data obtained by applying an adaptive filter to the
detected pulse wave data in FIG. 54 and the detected pressure data
in FIG. 55;
[0072] FIG. 57 shows the frequency analysis results obtained by
subjecting the residual data in FIG. 56 to FFT;
[0073] FIG. 58 is a schematic structural block diagram of a pulse
measurement device according to a third alternative of the second
embodiment;
[0074] FIG. 59 is an explanatory diagram of the arrangement of
sensors in a sensor module 111A of the third alternative of the
second embodiment;
[0075] FIG. 60 is an explanatory diagram of the arrangement of the
sensors in a sensor module 111B of the third alternative of the
second embodiment;
[0076] FIG. 61 is an explanatory diagram of the relationship
between the amount of change in height of the arm and the amount of
body motion components (amount of stroke components) included in
the pulse wave sensor output;
[0077] FIG. 62 is an explanatory diagram of the relationship
between the angle and direction of the arm;
[0078] FIG. 63 is an explanatory diagram of the relationship
between the amount of change in height of the arm position in the
arm position (direction of the arm) in its initial state and the
amount of body motion components (stroke components) as an angle
sensor output;
[0079] FIG. 64 is an explanatory diagram of the change in the
amount of body motion components (stroke components) as the angle
sensor output depending on the position of the arm when the amount
of change in height is fixed;
[0080] FIG. 65 is an explanatory diagram of the relationship
between the amount of change in height of the arm position in the
position of the arm (direction of the arm) in its initial state and
the amount of body motion components (stroke components) included
in the angle sensor output after correction;
[0081] FIG. 66A is a cross-sectional view of a pulse measurement
device of a third embodiment which is incorporated into a
watchcase;
[0082] FIG. 66B is a schematic structural block diagram of the
pulse measurement device of the third embodiment;
[0083] FIG. 66C shows a schematic structural block diagram of one
example of an adaptive filter of the third embodiment;
[0084] FIG. 67 is a schematic structural diagram of a differential
capacitive sensor, which is an angle sensor;
[0085] FIG. 68 is a partial enlarged diagram of the differential
capacitive sensor;
[0086] FIG. 69 is an explanatory diagram of the operation of the
differential capacitive sensor;
[0087] FIG. 70 is a front view of a rotary-spindle angle sensor
used as an angle sensor;
[0088] FIG. 71 is a side view of the rotary-spindle angle sensor in
FIG. 70;
[0089] FIG. 72 is a graph of a chronological arrangement of one
example of detected pulse wave data;
[0090] FIG. 73 shows the frequency analysis results obtained by
subjecting the detected pulse wave data in FIG. 72 to FFT;
[0091] FIG. 74 is a graph of a chronological arrangement of one
example of detected angle data;
[0092] FIG. 75 shows the frequency analysis results obtained by
subjecting the detected angle data in FIG. 74 to FFT;
[0093] FIG. 76 is a graph of a chronological arrangement of
residual data obtained by applying an adaptive filter to the
detected pulse wave data in FIG. 72 and the detected angle data in
FIG. 74;
[0094] FIG. 77 shows the frequency analysis obtained by subjecting
the residual data in FIG. 76 to FFT;
[0095] FIG. 78 is a graph of a chronological arrangement of one
example of corrected detected angle data;
[0096] FIG. 79 shows the frequency analysis obtained by subjecting
the corrected detected angle data to FFT;
[0097] FIG. 80 is a graph of a chronological arrangement of
residual data obtained by applying an adaptive filter to the
detected pulse wave data in FIG. 72 and the corrected detected
angle data in FIG. 78;
[0098] FIG. 81 shows the frequency analysis results obtained by
subjecting the residual data in FIG. 80 to FFT;
[0099] FIG. 82 is a diagram illustrating the principle of a blood
vessel simulation sensor mounted on the body and designed for
simulating the movement (behavior) of venous blood;
[0100] FIG. 83 is a schematic diagram of a first rigid type of
blood vessel simulation sensor;
[0101] FIG. 84 is a schematic diagram of a second rigid type of
blood vessel simulation sensor;
[0102] FIG. 85 is a schematic diagram of a first elastic type of
blood vessel simulation sensor;
[0103] FIG. 86 is a schematic diagram of a second elastic type of
blood vessel simulation sensor;
[0104] FIG. 87 is an explanatory diagram of the relationship
between a rigid type of blood vessel simulation sensor and the body
motion components (stroke components) included in the pulse wave
sensor output;
[0105] FIG. 88 is an explanatory diagram of the relationship
between an elastic type of blood vessel simulation sensor and the
body motion components (stroke components) included in the pulse
sensor output;
[0106] FIG. 89 is a schematic structural block diagram of a pulse
measurement device of the fourth embodiment;
[0107] FIG. 90 is an explanatory diagram of the arrangement of the
sensors in a sensor module of the pulse measurement device of the
fourth embodiment in a mounted state;
[0108] FIG. 91 is a schematic structural block diagram of the pulse
measurement device of the fourth embodiment;
[0109] FIG. 92 is a graph of a chronological arrangement of one
example of the detected pulse wave data according to the fourth
embodiment;
[0110] FIG. 93 is a graph in which detected pressure data
correlated with the detected pulse wave data in FIG. 92 is
chronologically arranged along the same time axis;
[0111] FIG. 94 is a graph of a chronological arrangement of
differential data calculated from the detected pulse wave data in
FIG. 92 and the detected pressure data in FIG. 93;
[0112] FIG. 95 shows the frequency analysis results obtained by
subjecting the differential data in FIG. 94 to FFT;
[0113] FIG. 96 is an explanatory diagram of the frequency analysis
results of the detected pulse wave data in a first alternative of
the fourth embodiment;
[0114] FIG. 97 is an explanatory diagram of the frequency analysis
results of detected pressure data;
[0115] FIG. 98 is an explanatory diagram of differential data,
which is the difference between detected pulse wave data after
analyzed for frequency and detected pressure data after analyzed
for frequency;
[0116] FIG. 99 is a schematic structural block diagram of one
example of an adaptive filter in a second alternative of the fourth
embodiment;
[0117] FIG. 100 is a graph of a chronological arrangement of one
example of the detected pulse wave data in the second alternative
of the fourth embodiment;
[0118] FIG. 101 is a graph in which pressure detection data
correlated with the detected pulse wave data in FIG. 100 is
chronologically arranged along the same time axis;
[0119] FIG. 102 is a graph of a chronological arrangement of
differential data obtained by applying an adaptive filter to the
detected pulse wave data in FIG. 100 and the detected pressure data
in FIG. 20;
[0120] FIG. 103 shows the frequency analysis results obtained by
subjecting the differential data in FIG. 102 to FFT;
[0121] FIG. 104A is an explanatory diagram of the arrangement of
sensors in a sensor module of a mounted pulse measurement device
according to a third alternative of the fourth embodiment, in a
mounted state;
[0122] FIG. 104B is a schematic structural block diagram of the
pulse measurement device according to the third alternative of the
fourth embodiment;
[0123] FIG. 105A is an explanatory diagram of the arrangement of
sensors in a sensor module of a pulse measurement device according
to a fourth alternative of the fourth embodiment, in a mounted
state;
[0124] FIG. 105B is a schematic structural block diagram of the
pulse measurement device according to the fourth alternative of the
fourth embodiment;
[0125] FIG. 106 is an explanatory diagram of the relationship
between acceleration in the direction of the X-axis described
hereinbelow, when a triaxial (X, Y, Z-axes) acceleration sensor is
used as an acceleration sensor, and the body motion components
(stroke components) included in the pulse wave sensor output
signal;
[0126] FIG. 107 is an explanatory diagram of the relationship
between acceleration in the direction of the Y-axis described
hereinbelow, when a triaxial acceleration sensor described
hereinbelow is used as an acceleration sensor, and the body motion
components (stroke components) included in the pulse wave sensor
output signal;
[0127] FIG. 108 is an explanatory diagram of the relationship
between acceleration in the direction of the Z-axis, when a
triaxial (X, Y, Z-axes) acceleration sensor described hereinbelow
is used as an acceleration sensor, and the body motion components
(stroke components) included in the pulse wave sensor output
signal;
[0128] FIG. 109 is an explanatory diagram of the three axes;
[0129] FIG. 110 is an external perspective view of a pulse
measurement device of a fifth embodiment;
[0130] FIG. 111 is a cross-sectional view of the sensor module in
FIG. 110;
[0131] FIG. 112 is an external perspective view of a case in which
a pulse measurement device of a sixth embodiment is incorporated in
a watchcase; and
[0132] FIG. 113 is a cross-sectional view of the pulse measurement
device in FIG. 112.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0133] Selected embodiments of the present invention will now be
explained with reference to the drawings. It will be apparent to
those skilled in the art from this disclosure that the following
descriptions of the embodiments of the present invention are
provided for illustration only and not for the purpose of limiting
the invention as defined by the appended claims and their
equivalents.
(1) First Embodiment
[0134] Referring to FIGS. 1 through 41, a pulse measurement device
10 will be described herein according to a first embodiment of the
present invention. First, the operational basis for the first
embodiment will be described prior to a detailed description of the
first embodiment.
[0135] The output from a pulse wave sensor for detecting pulse
waves includes various body motion components in addition to pulse
wave components. It is known that these body motion components are
generated by the changes in the body, particularly by the behavior
of venous blood, originating in the movement of the user
(walking/running, arm movement, and the like) whose pulse is to be
measured.
[0136] However, when a triaxial acceleration sensor is used for
detecting the body motion components, it is known that particularly
the body motion components in the peripheral direction, or,
specifically, in the direction of the X-axis, have a marked effect,
but the body motion components in the directions of the other two
axes (Y-axis and Z-axis) cannot be ignored. In view of this,
vectors are used in the present invention to represent the
accelerations along two axial directions when the same body motion
components are generated. Moreover, the relationship between the
amount of change in a combined vector of the two axial acceleration
vectors, and the amount of body motion components (amount of stroke
components) included in the output from the pulse wave sensor is
determined in the present invention.
[0137] FIG. 1 is an explanatory diagram of the relationship between
the amount of change in a combined vector of acceleration vectors
along two axes and the amount of body motion components (amount of
stroke components) included in the output of a pulse sensor. As
shown in FIG. 1, it is clear that the amount of change in the
combined vector of the two axial acceleration vectors and the
amount of body motion components (amount of stroke components)
included in the output of the pulse sensor have a substantially
proportional relationship. In other words, it is possible to
surmise the effect of the venous blood included in the output of
the pulse wave sensor if the amount of change in the combined
vector of the two axial vectors can be detected.
[0138] In view of this, in the first embodiment, the body motion
components originating in the veins are detected by a triaxial
acceleration sensor, and the pulse rate is accurately detected
based on a signal that is free of the effect of venous blood by
subtracting the detected output from the output of the pulse wave
sensor in a specific proportion.
[0139] FIG. 2 is an explanatory diagram of the manner in which the
pulse meter or pulse measurement device 10 of the first embodiment
is mounted. The pulse measurement device 10 is used while mounted
on the user's arm 11, and has a device main body (watchcase) 10A
and a wristband 10B for mounting the device main body 10A on the
arm. The pulse measurement device 10 according to the present
embodiment functions as a living organism information measurement
device mounted on the body and designed for measuring living
organism information, or as a wristwatch-type information device
mounted on the arm.
[0140] FIG. 3 is a cross-sectional view of the pulse measurement
device of the first embodiment. The back surface of the device main
body 10A is pressed against the back of the wrist when the pulse
measurement device 10 is mounted with the wristband 10B wound
around the wrist. The reverse side of the device main body 10A is
provided with a triaxial (X-axis, Y-axis, Z-axis) acceleration
sensor 12 and a pulse wave sensor 13. In this case, the triaxial
acceleration sensor 12 functions as a body motion sensor.
[0141] As shown in FIG. 3, the pulse wave sensor 13 has an LED 13A
for emitting light to detect pulse waves, a PD (Photo Detector) 13B
for receiving the detection light reflected by the body, and
transparent glass 13C for protecting the LED 13A and the PD 13B,
transmitting the light incident on the LED 13A and reflected light
obtained via the body, and directing the light onto the PD 13B. The
transparent glass 13C is fixed by means of a back lid 14 as a
component of the device main body 10A. The configuration of this
pulse wave sensor 13 is designed such that light from the LED 13A
is reflected from the back of the wrist through the transparent
glass 13C, and the reflected light is received by the photo
detector 13B.
[0142] The front side of the device main body 10A is provided with
a liquid crystal display device 15 for displaying the pulse rate HR
and other such living organism information based on the detection
results from the pulse wave sensor 13 in addition to the current
time and date. Also, the interior of the device main body 10A has a
CPU and other such IC circuits on a main board 16, whereby a data
processing circuit 17 is configured.
[0143] Also, the reverse side of the main board 16 is provided with
a battery 18, which supplies power to the triaxial acceleration
sensor, the pulse wave sensor 13, the liquid crystal display device
15, and the main board 16.
[0144] The triaxial acceleration sensor 12 and the pulse wave
sensor 13 are connected with the main board 16 by a heat seal 19.
Power is supplied from the main board 16 to the triaxial
acceleration sensor 12 and the pulse wave sensor 13 through a
wiring formed by the heat seal 19. As a result, an acceleration
detection signal is fed from the triaxial acceleration sensor 12 to
the main board 16. Also, a pulse wave detection signal is fed from
the pulse wave sensor 13 to the main board 16.
[0145] The data processing circuit 17 subjects the acceleration
detection signal and the pulse wave detection signal to FFT
processing, and the pulse rate HR is calculated by analyzing the
processing results. The external surface of the device main body
10A is provided with a plurality of button switches 20A, 20B, 20C,
20D, and 20E for time setting, display mode switching, and the
like, as shown in FIG. 1.
[0146] FIG. 4 is a schematic structural block diagram of the pulse
measurement device 10 of the first embodiment. In general terms,
the pulse measurement device 10 has a pulse wave signal amplifying
circuit 21, an acceleration signal amplifying circuit 22, an A/D
conversion circuit 23, and an MPU 24, a RAM 25, and a ROM 26 in
addition to the triaxial acceleration sensor 12, the pulse wave
sensor 13, and the liquid crystal display device 15 described
above. Essentially, the pulse wave sensor 13, the pulse wave signal
amplifying circuit 21, and the A/D conversion circuit 23 together
constitute a pulse wave detecting section. The triaxial
acceleration sensor 12, the acceleration signal amplifying circuit
22, and the A/D conversion circuit 23 together constitute a body
motion detecting section.
[0147] The triaxial acceleration sensor 12 has an X-axis
acceleration sensor 12X for detecting acceleration in the direction
of the X-axis, a Y-axis acceleration sensor 12Y for detecting
acceleration in the direction of the Y-axis, and a Z-axis
acceleration sensor 12Z for detecting acceleration in the direction
of the Z-axis shown in FIG. 1 or 2.
[0148] The pulse wave signal amplifying circuit 21 amplifies the
pulse wave detection signal outputted from the pulse wave sensor 13
at a prescribed rate of amplification, and outputs the result to
the A/D conversion circuit 23 as an amplified pulse wave detection
signal.
[0149] The acceleration signal amplifying circuit 22 amplifies the
X-axis acceleration detection signal, the Y-axis acceleration
detection signal, and the Z-axis acceleration detection signal
outputted from the triaxial acceleration sensor 12 at a prescribed
rate of amplification, and outputs the result to the A/D conversion
circuit 23 as an amplified X-axis acceleration detection signal, an
amplified Y-axis acceleration detection signal, and an amplified
Z-axis acceleration detection signal.
[0150] The A/D conversion circuit 23 performs analog/digital
conversion separately on the inputted amplified pulse wave
detection signal, the amplified X-axis acceleration detection
signal, the amplified Y-axis acceleration detection signal, the
amplified Z-axis acceleration detection signal, and the amplified
pressure detection signal, and outputs the result to the MPU 24 as
detected pulse wave data or pulse wave detection data, detected
X-axis acceleration data Kx, Y-axis acceleration data Ky, and
Z-axis acceleration data Kz.
[0151] The MPU 24 stores the detected X-axis acceleration data Kx,
the detected Y-axis acceleration data Ky, and the detected Z-axis
acceleration data Kz in the RAM 25, calculates the pulse rate based
on a control program stored in the ROM 26, and displays the result
on the display device 15.
[0152] More specifically, the MPU 24 chronologically arranges the
detected pulse wave data stored in the RAM 25 as well as detected
body motion data or body motion detection data obtained based on
the detected X-axis acceleration data Kx, the detected Y-axis
acceleration data Ky, and the detected Z-axis acceleration data Kz,
and determines residual data, which is the difference between the
detected pulse wave data and the detected body motion data for each
sampling time. Frequency analysis (FFT: Fast Fourier
Transformation) is then performed on the residual data, the
harmonic components of the pulse wave are extracted, and the pulse
rate is calculated from the frequency. Therefore, the MPU 24 also
essentially functions as a body motion component generating
section.
[0153] A more specific pulse rate calculation process will now be
described.
[0154] FIG. 5 is a schematic structural block diagram of one
example of an adaptive filter 30 of the first embodiment. In
general terms, the adaptive filter 30 has a filter coefficient
generating section 31 and a synthesizer 32.
[0155] A coefficient controller 31A of the filter coefficient
generating section 31 functions as a body motion component removing
section and generates an adaptive filter coefficient h based on
previously outputted data by the synthesizer 32 to which the filter
has been applied. The filter coefficient generating section 31 then
applies the adaptive filter coefficient h generated by the
coefficient controller 31A to a simulated low-frequency signal (=z)
and to combined acceleration vector data (=y), which is the
combined data of the X-axis acceleration data Kx, the Y-axis
acceleration data Ky, and the Z-axis acceleration data Kz as
inputted body motion component detection signals; then generates
body motion removal data h(x), h(y), and h(z); and outputs the
result to the synthesizer 32.
[0156] The synthesizer 32 functions as a removal processing
section; combines the extracted detected pulse wave data (=pulse
wave components+body motion components) and the body motion removal
data h(x), h(y), and h(z); substantially removes (subtracts) the
body motion components contained in the current detected pulse wave
data; and extracts pulse wave components e(n).
[0157] The reasons for using a simulated low-frequency signal will
now be described. According to the experiments in developing the
present invention, sometimes low-frequency fluctuating components
remained in the resulting pulse wave components, and the pulse rate
could not be accurately determined even when the body motion
removal data h(x) and h(y) was removed from the detected pulse wave
data. This is thought to be the effect of breathing and nerve
activity, but to detect these signals and remove their effect would
require a large and bulky system and would make it impossible to
achieve a portable pulse measurement device.
[0158] In the present invention, these effects were removed by
applying an adaptive filter upon multiplying the output signal from
the triaxial acceleration sensor 12, which is the body motion
detection sensor, by a simulated low-frequency signal whose
frequency corresponds to a low-frequency variation component. In
this case, the simulated low-frequency signal must have a specific
frequency distribution during frequency analysis and remove
low-frequency variation components, and should be a triangular or
rectangular wave of 0.5 Hz or less in view of the fact that the
frequency band thereof is 0.5 Hz or less. The frequency band and
the waveform can be appropriately varied in accordance with the
actually contained low-frequency variation components.
[0159] The specific pulse rate detection process of the first
embodiment will now be described with reference to FIGS. 6 through
19.
[0160] FIG. 6 is a graph showing a chronological arrangement of
X-axis acceleration data Kx for the X-axis acceleration detection
signal outputted from the X-axis acceleration sensor 12X. FIG. 7
shows the frequency analysis results obtained by subjecting the
detected X-axis acceleration data Kx in FIG. 6 to FFT.
[0161] FIG. 8 is a graph showing a chronological arrangement of
Y-axis acceleration data Ky for the Y-axis acceleration detection
signal outputted from the Y-axis acceleration sensor 12Y. FIG. 9
shows the frequency analysis results obtained by subjecting the
detected Y-axis acceleration data Ky in FIG. 8 to FFT.
[0162] FIG. 10 is a graph showing a chronological arrangement of
Z-axis acceleration data Kz for the Z-axis acceleration detection
signal outputted from the Z-axis acceleration sensor 12Z. FIG. 11
shows the frequency analysis results obtained by subjecting the
detected Z-axis acceleration data Kz in FIG. 10 to FFT.
[0163] It is clear from comparing FIGS. 6, 8, and 10 that the
effect of the X-axis acceleration components is greater than the
effect of the Y-axis acceleration components or the Z-axis
acceleration components. Therefore, the inventors decided to treat
the Y-axis acceleration components and the Z-axis acceleration
components in an integral manner, as described above, and to detect
the amount of change in the combined vector of the acceleration
vectors along two axes with the intention of simplifying the
process while maintaining measuring precision.
[0164] FIG. 12 is a graph obtained by treating the Y-axis
acceleration data Ky corresponding to the Y-axis acceleration
detection signal outputted from the Y-axis acceleration sensor 12Y,
and the Z-axis acceleration data Kz corresponding to the Z-axis
acceleration detection signal outputted from the Z-axis
acceleration sensor 12Z as vectors, and chronologically arranging
combined acceleration vector data obtained as a combined vector
thereof. FIG. 13 shows the frequency analysis results obtained by
subjecting the combined acceleration vector data (={square
root}(Ky.sup.2+Kz.sup.2)), or, specifically, the biaxial
acceleration combined components in FIG. 12 to FFT.
[0165] FIG. 14 is a graph showing a chronological arrangement of a
preset simulated low-frequency signal (using a triangular wave).
FIG. 15 shows the frequency analysis results obtained by subjecting
the simulated low-frequency signal in FIG. 14 to FFT. As can be
seen from FIG. 15, the frequency is approximately 0.5 Hz or less,
with a specific frequency distribution.
[0166] FIG. 16 is a graph of a chronological arrangement of one
example of the detected pulse data. FIG. 17 shows the frequency
analysis results obtained by subjecting the detected pulse data in
FIG. 16 to FFT.
[0167] First, the MPU 24 sequentially reads the detected pulse wave
data, the detected X-axis acceleration data, the detected Y-axis
acceleration data, and the detected Z-axis acceleration data stored
in the RAM 25, and outputs the detected pulse wave data in a single
sampling period to the synthesizer 32. In parallel with this, the
MPU 24 outputs the detected X-axis acceleration data Kx, the
detected Y-axis acceleration data Ky, and the detected Z-axis
acceleration data Kz corresponding to the detected pulse wave data
outputted to the synthesizer 32 to the filter coefficient
generating section 31.
[0168] Thus, the coefficient controller 31A of the filter
coefficient generating section 31 generates the adaptive filter
coefficient h based on previously outputted data by the synthesizer
32 to which the filter has been applied. Under control from the
coefficient controller 31A, the filter coefficient generating
section 31 then applies the adaptive filter coefficient h to the
simulated low-frequency signal (=z), to the detected X-axis
acceleration data Kx (=x), and to combined acceleration vector data
(=y), which is the combined data of the Y-axis acceleration data Ky
and the Z-axis acceleration data Kz, inputted as body motion
component detection signals; generates body motion removal data
h(x), h(y), and h(z); and outputs the result to the synthesizer
32.
[0169] Thus, the synthesizer 32 combines the current pulse wave
data and the body motion removal data h(x), h(y), and h(z);
substantially removes (subtracts) the body motion components
contained in the current detected pulse wave data; extracts the
pulse wave components; and outputs the residual data e(n), which is
the data to which the adaptive filter has been applied.
[0170] FIG. 18 is a graph plotted as a result of a chronological
arrangement of the residual data obtained by combining the signals
obtained by applying an adaptive filter to the amplified X-axis
acceleration detection signal in FIG. 6, the combined acceleration
vector signal in FIG. 12, and the simulated low-frequency signal in
FIG. 14 for the pulse wave detection signal in FIG. 16.
[0171] Next, the MPU 24 subjects the residual data to FFT. FIG. 19
shows the frequency analysis results obtained by subjecting the
residual data in FIG. 18 to FFT. Thus, the frequency analysis
results thus obtained have the body motion components originating
in the veins substantially removed from the output signal (pulse
wave components+body motion components) of the pulse wave sensor,
and are, specifically, pulse wave data that primarily corresponds
to the pulse wave components.
[0172] For the sake of comparison, pulse wave data obtained when a
simulated low-frequency signal has not been used will now be
described.
[0173] FIG. 20 is a graph of a chronological arrangement of
residual data obtained by combining the signals obtained by
applying an adaptive filter to the amplified X-axis acceleration
detection signal in FIG. 6 and the combined acceleration vector
signal in FIG. 12 for the pulse wave detection signal in FIG. 16.
FIG. 21 shows the frequency analysis results obtained by subjecting
the residual data in FIG. 20 to FFT.
[0174] It can readily be seen by comparing FIGS. 19 and 21 that
low-frequency variation components can be reduced in accordance
with the configuration of the first embodiment, and that the effect
of low-frequency variation components in pulse rate detection can
therefore be removed with ease.
[0175] Furthermore, the MPU 24 calculates the pulse rate from the
frequency on the assumption that the maximum frequency components
of the resulting pulse wave data primarily containing pulse wave
components constitute the pulse spectrum. Therefore, the MPU 24
functions as a pulse rate calculating section. The MPU 24 then
displays the pulse rate on the liquid crystal display device
15.
[0176] Furthermore, the MPU 24 can also be configured so as to
calculate the pitch or the number of steps of the user from the
detected body motion components. In this case, the MPU 24 functions
as a body motion information detecting section for detecting the
pitch or the number of steps.
[0177] As described above, according to the first embodiment,
variation in the veins, which is the main factor in the body motion
components generated in the body, can be surely detected and
registered by using the pulse wave sensor 13 and the triaxial
acceleration sensor 12 functioning as a body motion sensor, and
also by using a simulated low-frequency signal. Therefore, the body
motion components can be surely removed, making it possible to
accurately detect pulse wave components, and hence to accurately
measure the pulse rate.
[0178] (1.1) First Alternative of the First Embodiment
[0179] A pulse measurement device according to the first
alternative of the first embodiment is similar to the first
embodiment, except that the first embodiment uses combined
acceleration vector data (={square root}(Ky.sup.2+Kz.sup.2)), which
is the combined data of the Y-axis acceleration data Ky and the
Z-axis acceleration data Kz, while the first alternative uses
combined acceleration vector data (={square
root}(Kx.sup.2+Ky.sup.2+Kz.sup.2)), which is a combination of the
following three types of acceleration data: the X-axis acceleration
data, the Y-axis acceleration data, and the Z-axis acceleration
data, specifically, the combined components of triaxial
acceleration. Therefore, the configuration of the first alternative
of the first embodiment is essentially the same as the
configuration of the pulse measurement device 10 shown in FIGS. 2
through 4, except that the MPU 24 is configured with an adaptive
filter 40 of the first alternative instead of being configured with
the adaptive filter 30 of the first embodiment.
[0180] FIG. 22 is a schematic structural block diagram of one
example of the adaptive filter 40 of the first alternative of the
first embodiment. In general terms, the adaptive filter 40 has a
filter coefficient generating section 41, an integrator 42, and a
synthesizer 43.
[0181] The filter coefficient generating section 41 functions as a
body motion component removing section, and generates an adaptive
filter coefficient h based on data previously outputted by the
synthesizer 43 after the filter has been applied.
[0182] In parallel with this, the integrator 42 multiplies the
combined acceleration vector data (={square
root}(Kx.sup.2+Ky.sup.2+Kz.sup.2)), which is a combination of the
following three types of acceleration data: X-axis acceleration
data, Y-axis acceleration data, and Z-axis acceleration data, by a
preset simulated low-frequency signal, and outputs the result to
the filter coefficient generating section 41.
[0183] As a result, the filter coefficient generating section 41
applies the generated adaptive filter coefficient h to the output
from the integrator 42, generates body motion removal data
h(Kx.sup.2+Ky.sup.2+Kz.- sup.2), and outputs the result to the
synthesizer 43.
[0184] The synthesizer 43 functions as a removal processing
section; combines the extracted detected pulse wave data (=pulse
wave components+body motion components) with the body motion
removal data h(Kx.sup.2+Ky.sup.2+Kz.sup.2), substantially removes
(subtracts) the body motion components contained in the current
detected pulse wave data, and extracts the residual data e(n).
[0185] The specific pulse rate calculating process of the first
alternative will now be described.
[0186] FIG. 23 is a graph of a chronological arrangement of
detected X-axis acceleration data Kx for the X-axis acceleration
detection signal outputted from the X-axis acceleration sensor 12X.
FIG. 24 shows the frequency analysis results obtained by subjecting
the detected X-axis acceleration data Kx in FIG. 23 to FFT.
[0187] FIG. 25 is a graph of a chronological arrangement of Y-axis
acceleration data Ky for the Y-axis acceleration detection signal
outputted from the Y-axis acceleration sensor 12Y. FIG. 26 shows
the frequency analysis results obtained by subjecting the detected
Y-axis acceleration data Ky in FIG. 25 to FFT.
[0188] FIG. 27 is a graph of a chronological arrangement of Z-axis
acceleration data Kz for the Z-axis acceleration detection signal
outputted from the Z-axis acceleration sensor 12Z. FIG. 28 shows
the frequency analysis results obtained by subjecting the detected
Z-axis acceleration data Kz in FIG. 27 to FFT.
[0189] FIG. 29 is a graph of a chronological arrangement of
combined acceleration vector data (={square
root}(Kx.sup.2+Ky.sup.2+Kz.sup.2)) obtained as a combined
acceleration vector by treating the X-axis acceleration data Kx
outputted from the X-axis acceleration sensor 12X, the Y-axis
acceleration data Ky corresponding to the Y-axis acceleration
detection signal outputted from the Y-axis acceleration sensor 12Y,
and the Z-axis acceleration data Kz for the Z-axis acceleration
detection signal outputted from the Z-axis acceleration sensor 12Z
as vectors. FIG. 30 shows the frequency analysis results obtained
by subjecting the combined acceleration vector data (={square
root}(Kx.sup.2+Ky.sup.2+Kz.su- p.2)) in FIG. 29 to FFT.
[0190] FIG. 31 is a graph of a chronological arrangement of one
example of the detected pulse wave data. FIG. 32 shows the
frequency analysis results obtained by subjecting the detected
pulse wave data in FIG. 31 to FFT.
[0191] First, the MPU 24 sequentially reads the detected pulse wave
data, the detected X-axis acceleration data, the detected Y-axis
acceleration data, and the detected Z-axis acceleration data stored
in the RAM 25, and outputs the detected pulse wave data in a single
sampling period to the synthesizer 43.
[0192] In parallel with this, the MPU 24 outputs the detected
X-axis acceleration data Kx, the detected Y-axis acceleration data
Ky, and the detected Z-axis acceleration data Kz corresponding to
the detected pulse wave data outputted to the synthesizer 43 to the
integrator 42.
[0193] The integrator 42 multiplies the combined acceleration
vector data (={square root}(Kx.sup.2+Ky.sup.2+Kz.sup.2)), which is
a combination of the following three types of acceleration data:
the X-axis acceleration data, the Y-axis acceleration data, and the
Z-axis acceleration data, by a simulated low-frequency signal such
as the one shown in FIGS. 14 and 15; and outputs the result to the
filter coefficient generating section 41.
[0194] Thus, the filter coefficient generating section 41 generates
the adaptive filter coefficient h based on the previously outputted
data by the synthesizer 43 to which the filter has been
applied.
[0195] The filter coefficient generating section 41 then applies
the adaptive filter coefficient h to the inputted combined
acceleration vector data (={square
root}(Kx.sup.2+Ky.sup.2+Kz.sup.2)), generates body motion removal
data h(Kx.sup.2+Ky.sup.2+Kz.sup.2), and outputs the result to the
synthesizer 43.
[0196] Thus, the synthesizer 43 combines the current pulse wave
data with the body motion removal data
h(Kx.sup.2+Ky.sup.2+Kz.sup.2), substantially removes (subtracts)
the body motion components contained in the current detected pulse
wave data, extracts the pulse wave components, and outputs the
residual data, which is the data to which the adaptive filter has
been applied.
[0197] FIG. 33 is a graph of a chronological arrangement of the
residual data obtained by combining the data obtained by applying
an adaptive filter to the combined acceleration vector data in FIG.
29 and the simulated low-frequency signal in FIG. 14 for the
detected pulse wave data in FIG. 31.
[0198] Next, the MPU 24 subjects the residual data to FFT.
[0199] FIG. 34 shows the frequency analysis results obtained by
subjecting the residual data in FIG. 33 to FFT.
[0200] Thus, the frequency analysis results thus obtained retain
spectra unrelated to the pulse wave components in a lower frequency
range (<0.5 Hz) in comparison with the first embodiment, but
they do not have any effect on the frequency band of the pulse wave
components (2 Hz to 2.5 Hz). Therefore, the results have the body
motion components originating in the veins substantially removed
from the output signal of the pulse wave sensor (pulse wave
components+body motion components), or, specifically, the results
constitute pulse wave data corresponding primarily to the pulse
wave components.
[0201] (1.2) Second Alternative of the First Embodiment
[0202] A pulse measurement device in a second alternative of the
first embodiment is similar to the first embodiment, except that
the use of a simulated low-frequency signal in the first embodiment
is avoided in order to simplify the process and the device
configuration, and that the use of combined acceleration vector
data obtained by combining the Y-axis acceleration data and the
Z-axis acceleration data is avoided as well. Therefore, the
configuration in the second alternative of the first embodiment is
essentially the same as the configuration of the pulse measurement
device 10 shown in FIGS. 2 through 4, except that the MPU 24 is
configured with an adaptive filter 50 of the second alternative
instead of being configured with the adaptive filter 30 of the
first embodiment.
[0203] FIG. 35 is a schematic structural block diagram of one
example of an adaptive filter 50 according to the second
alternative of the first embodiment. In general terms, the adaptive
filter 50 has a filter coefficient generating section 51 and a
synthesizer 52.
[0204] A coefficient controller 51A of the filter coefficient
generating section 51 functions as a body motion component removing
section and generates the adaptive filter coefficient h based on
the data previously outputted from the synthesizer 52 to which the
adaptive filter has been applied.
[0205] The filter coefficient generating section 51 applies the
adaptive filter coefficient h generated by the coefficient
controller 51 A to the X-axis acceleration data Kx, the Y-axis
acceleration data Ky, and the Z-axis acceleration data Kz, which
are the inputted body motion component detection signals; generates
body motion removal data h(x), h(y), and h(z); and outputs the
result to the synthesizer 52.
[0206] The synthesizer 52 functions as a removal processing
section; combines the extracted detected pulse wave data (=pulse
wave components+body motion components) with the body motion
removal data h(x), h(y), and h(z); substantially removes
(subtracts) the body motion components contained in the current
detected pulse wave data; and extracts the pulse wave components
e(n).
[0207] An example of specific processed data will now be
described.
[0208] FIG. 36 is a graph of a chronological arrangement of one
example of detected pulse wave data. FIG. 37 shows the frequency
analysis results obtained by subjecting the detected pulse wave
data in FIG. 36 to FFT.
[0209] FIG. 38 is a graph of a chronological arrangement of
residual data obtained by combining the signals obtained by
applying an adaptive filter to the combined acceleration vector
signal in FIG. 29 and the simulated low-frequency signal in FIG. 14
for the detected pulse wave data in FIG. 31. FIG. 39 shows the
frequency analysis results obtained by subjecting the residual data
in FIG. 38 to FFT.
[0210] The MPU 24 subjects the residual data e(n) to FFT, whereby,
as shown in FIG. 34, the frequency analysis results thus obtained
have the body motion components originating in the veins
substantially removed from the output signal of the pulse wave
sensor (pulse wave components+body motion components) similar to
the first embodiment, or, specifically, the results constitute
pulse wave data corresponding primarily to the pulse wave
components. Also, in the second alternative of the first
embodiment, the process and device configuration can be simplified
because a simulated low-frequency signal is not used for
processing.
[0211] (1.3) Third Alternative of the First Embodiment
[0212] A third alternative of the first embodiment is similar to
the first alternative of the first embodiment except for dispensing
with the use of a simulated low-frequency signal to conduct
processing in the first alternative of the first embodiment (1.1).
Therefore, the configuration in the third alternative of the first
embodiment is essentially the same as the configuration of the
pulse measurement device 10 shown in FIGS. 2 through 4, except that
the MPU 24 is configured with an adaptive filter 60 of the third
alternative instead of being configured with the adaptive filter 30
of the first embodiment.
[0213] FIG. 40 is a schematic structural block diagram of one
example of the adaptive filter 60 according to the third
alternative of the first embodiment. In general terms, the adaptive
filter 60 has a filter coefficient generating section 61 and a
synthesizer 62.
[0214] The filter coefficient generating section 61 functions as a
body motion component removing section that generates the adaptive
filter coefficient h based on the data previously outputted from
the synthesizer 62 to which the adaptive filter has been applied.
Furthermore, the filter coefficient generating section 61 applies
the adaptive filter coefficient h generated by the combined
acceleration vector data (={square
root}(Kx.sup.2+Ky.sup.2+Kz.sup.2)), which is a combination the
following three types of acceleration data: the X-axis acceleration
data Kx, the Y-axis acceleration data Ky, and the Z-axis
acceleration data Kz; generates body motion removal data
h(Kx.sup.2+Ky.sup.2+Kz.sup.2); and outputs the result to the
synthesizer 62.
[0215] The synthesizer 62 functions as a removal processing
section; combines the extracted detected pulse wave data (=pulse
wave components+body motion components) with the body motion
removal data h(Kx.sup.2+Ky.sup.2+Kz.sup.2); substantially removes
(subtracts) the body motion components contained in the current
detected pulse wave data; and extracts the pulse wave components
e(n).
[0216] According to the third alternative of the first embodiment,
it is possible to obtain the same effects as in the first
alternative of the first embodiment, and it is also possible to
further simplify the device structure and the processing because a
simulated low-frequency signal is not used.
[0217] (1.4) Fourth Alternative of the First Embodiment
[0218] A fourth alternative of the first embodiment is similar to
the first embodiment, except for dispensing with use of a simulated
low-frequency signal to conduct processing in the first embodiment.
Therefore, the configuration in the fourth alternative of the first
embodiment is essentially the same as the configuration of the
pulse measurement device 10 shown in FIGS. 2 through 4, except that
the MPU 24 is configured with an adaptive filter 70 of the fourth
alternative instead of being configured with the adaptive filter 30
of the first embodiment.
[0219] FIG. 41 is a schematic structural block diagram of one
example of the adaptive filter 70 according to the first
embodiment. In general terms, the adaptive filter 70 has a filter
coefficient generating section 71 and a synthesizer 72.
[0220] A coefficient controller 71A of the filter coefficient
generating section 71 functions as a body motion component removing
section that generates the adaptive filter coefficient h based on
the data previously outputted from the synthesizer 72 to which the
adaptive filter has been applied. The filter coefficient generating
section 71 applies the adaptive filter coefficient h generated by
the coefficient controller 71A to the detected X-axis acceleration
data Kx (=x) and to the combined acceleration vector data (=y)
consisting of the combined data from the Y-axis acceleration data
Ky and the Z-axis acceleration data Kz, which are the inputted body
motion component detection signals; generates body motion removal
data h(x), h(y), and h(z); and outputs the result to the
synthesizer 72.
[0221] The synthesizer 72 functions as a removal processing
section; combines the extracted detected pulse wave data (=pulse
wave components+body motion components) with the body motion
removal data h(x), h(y), and h(z); substantially removes
(subtracts) the body motion components contained in the current
detected pulse wave data; and extracts the pulse wave components
e(n).
[0222] According to the fourth alternative of the first embodiment,
it is possible to obtain the same effects as in the first
embodiment, and it is also possible to further simplify the device
structure and the processing because a simulated low-frequency
signal is not used.
[0223] In the first embodiment and in the first alternative and the
third through fifth alternatives of the first embodiment, no
weighting was done when calculating the combined acceleration
vector data (={square root}(Kx.sup.2+Ky.sup.2+Kz.sup.2)), which is
a combination of the following three types of acceleration data:
the X-axis acceleration data Kx, the Y-axis acceleration data Ky,
and the Z-axis acceleration data Kz, or when calculating the
combined acceleration vector data (={square
root}Ky.sup.2+Kz.sup.2), which is a combination of the following
two types of acceleration data: the Y-axis acceleration data Ky and
the Z-axis acceleration data Kz; but it is also possible to use a
configuration such that the acceleration data constituting the
basis of all the combined acceleration vector data is suitably
weighted.
[0224] For example, the following formula can be used when
determining the combined acceleration vector data from the
following three types of acceleration data: the X-axis acceleration
data Kx, the Y-axis acceleration data Ky, and the Z-axis
acceleration data Kz.
{square root}(a.multidot.Kx.sup.2+b.multidot.Ky.sup.2+cKz.sup.2);
wherein a>b>c.gtoreq.0.
[0225] Also, the X-axis acceleration data Kx, the Y-axis
acceleration data Ky, and the Z-axis acceleration data Kz may
similarly be suitably weighted and the adaptive filter coefficient
may be applied thereto even when the combined acceleration vector
data is not used, as in the second alternative of the first
embodiment.
[0226] Furthermore, the simulated low-frequency signal may also be
weighted.
[0227] Furthermore, as shown in FIG. 2, in the above descriptions,
the case of fitting the triaxial acceleration sensor 12 on the arm
was described, but it is also possible to mount the sensor on the
base of the fingers or the fingertips.
(2) Second Embodiment
[0228] A pulse measurement device 80 according to a second
embodiment of the present invention will now be described with
reference to FIGS. 42 through 60. The main difference between the
second embodiment and the first embodiment is that the body motion
components are measured in the second embodiment using a pressure
sensor instead of the triaxial acceleration sensor of the first
embodiment. Otherwise the basic configuration is similar to the
first embodiment; therefore, in view of the similarity between the
first embodiment and the second embodiment, descriptions of the
parts of the second embodiment with identical or similar functions
to the parts of the first embodiment are omitted for the sake of
simplicity.
[0229] First, the operating principle of the second embodiment will
be described prior to a detailed description of the second
embodiment.
[0230] The output of the pulse wave sensor for detecting pulse
waves includes various body motion components in addition to pulse
wave components. It is known that these body motion components are
generated by changes in the body originating in the movements
(walking/running, arm movement, and the like) of the user whose
pulse is to be measured. Therefore, it is possible to detect the
movements of the user when an acceleration sensor is used as the
sensor for detecting body motion components, but the body motion
components contained in the output of the pulse wave sensor are
generated by changes in the body originating from these movements,
and it is difficult to accurately detect the true body motion
components contained in the output of the pulse wave sensor.
[0231] The effect of venous blood as a body motion component
generated in the body cannot be ignored because this component has
the greatest effect on an optical sensor used as a pulse wave
sensor.
[0232] It is known that since the vein walls are highly extensible,
they are stretched out when blood pressure increases, large
quantities of blood accumulate in these sections, and this process
is accompanied by an increase of pressure on the body surface along
with the stretching of the veins. The inventors have accordingly
researched the relationship between the amount of change in
pressure on the body surface and the amount of body motion
components (amount of stroke components) included in the pulse wave
sensor when the same body motion components are generated.
[0233] FIG. 42 is an explanatory diagram of the relationship
between the amount of change in pressure and the amount of body
motion components (amount of stroke components) included in the
pulse wave sensor output. As shown in FIG. 42, it is clear that the
amount of change in pressure and the amount of body motion
components (amount of stroke components) included in the pulse wave
sensor output have an essentially proportional relationship. In
other words, it is possible to surmise the effect of the venous
blood included in the output of the pulse wave sensor if the amount
of change in pressure in the body surface can be detected.
[0234] In view of this, in the second embodiment, the pulse rate is
accurately detected based on a signal from which the effect of
venous blood has been removed by detecting the stretching of the
veins, or, specifically, the body motion components originating in
the veins with an external pressure sensor, and subtracting them
from the pulse wave sensor output at a specific rate.
[0235] The second embodiment will now be described in detail. FIG.
43 is a schematic structural diagram of a pulse measurement device
80 of the second embodiment. In general terms, the pulse
measurement device 80 has a sensor module 81 mounted on the finger
of the user, and a device main body 82 connected to the sensor
module 81 via a wiring L and mounted on the arm of the user.
[0236] FIG. 44 is an explanatory diagram of the arrangement of
sensors in the sensor module 81. In general terms, the sensor
module 81 is configured with a pulse wave sensor 83 for primarily
detecting pulse wave components and a pressure sensor 84 for
primarily detecting body motion components.
[0237] The pulse wave sensor 83 has an LED 83A for emitting
detection light and a PD (Photo Detector) 83B for receiving the
detection light reflected by the body.
[0238] FIG. 45 is a schematic structural block diagram of the pulse
measurement device 80. In general terms, the pulse measurement
device 80 has a pulse wave signal amplifying circuit 91, a body
motion signal amplifying circuit 92, an A/D conversion circuit 93,
an MPU 94, a RAM 95, a ROM 96, and a liquid crystal display device
or other such display device 97 in addition to the pulse wave
sensor 83 and the pressure sensor 84 previously described. As
described above, the pressure sensor 84 is used as the body motion
sensor in the second embodiment.
[0239] The pulse wave signal amplifying circuit 91 amplifies the
pulse wave detection signal outputted from the pulse wave sensor 83
at a prescribed rate of amplification, and outputs the result to
the A/D conversion circuit 93 as an amplified pulse wave detection
signal.
[0240] The body motion signal amplifying circuit 92 amplifies the
pressure detection signal outputted from the pressure sensor 84 at
a prescribed rate of amplification, and outputs the result to the
A/D conversion circuit 93 as an amplified pressure detection
signal.
[0241] The A/D conversion circuit 93 performs analog/digital
conversion separately on the inputted amplified pulse wave
detection signal and the amplified pressure detection signal, and
outputs the result to the MPU 94 as detected pulse wave data and
detected pressure data.
[0242] The MPU 94 stores the detected pulse wave data and the
detected pressure data (detected body motion data) in the RAM 95,
calculates the pulse rate based on a control program stored in the
ROM 96, and displays the result on the display device 97. More
specifically, the MPU 94 chronologically arranges the detected
pulse wave data and the detected pressure data (detected body
motion data) stored in the RAM 95, and determines the differential
data, which is the difference between the detected pulse wave data
and the detected pressure data, for each corresponding sampling
time.
[0243] Frequency analysis (FFT: Fast Fourier Transformation) is
then performed on the differential data, the harmonic components of
the pulse wave are extracted, and the pulse rate is calculated from
the frequency.
[0244] A more specific pulse rate calculation process will now be
described.
[0245] FIG. 46 is a graph of a chronological arrangement of one
example of detected pulse wave data. FIG. 47 is a graph in which
detected pressure data correlated with the detected pulse wave data
in FIG. 46 is chronologically arranged along the same time
axis.
[0246] First, the MPU 94 sequentially reads out the detected pulse
wave data and the detected pressure data stored in the RAM 95 and
calculates the differential data by subtracting the detected
pressure data in a certain sampling period from the detected pulse
wave data for the same sampling timing.
[0247] FIG. 48 is a graph of a chronological arrangement of
differential data calculated from the detected pulse wave data in
FIG. 46 and the detected pressure data in FIG. 47.
[0248] Next, the MPU 94 subjects the differential data to FFT.
[0249] FIG. 49 shows the frequency analysis results obtained by
subjecting the differential data in FIG. 48 to FFT.
[0250] Thus, the frequency analysis results thus obtained have the
body motion components originating in the veins substantially
removed from the output signal (pulse wave components+body motion
components) of the pulse wave sensor, and are, specifically, pulse
wave data that primarily corresponds to the pulse wave
components.
[0251] Furthermore, the MPU 94 calculates the pulse rate from the
frequency on the assumption that the maximum frequency components
of the resulting pulse wave data constitute the pulse spectrum.
[0252] The MPU 94 then displays the pulse rate on the display
device 97.
[0253] As described above, according to the second embodiment,
variation in the veins, which is the main factor in the body motion
components generated in the body, can be accurately detected and
registered by using a pressure sensor. Therefore, the body motion
components can be accurately removed, making it possible to
accurately detect pulse wave components, and hence to accurately
measure the pulse rate.
[0254] (2.1) First Alternative of the Second Embodiment
[0255] A first alternative of the second embodiment is similar to
the second embodiment, except that the second embodiment has a
configuration in which the differential data is calculated by
subtracting the detected pressure data from the detected pulse wave
data prior to frequency analysis (FFT), while in the first
alternative, the differential data is calculated after performing
frequency analysis on the detected pulse wave data and the detected
pressure data. Therefore, the configuration of the first
alternative of the second embodiment is essentially the same as the
configuration of the pulse measurement device 80 of the second
embodiment shown in FIGS. 43 through 45.
[0256] In the first alternative of the second embodiment, the MPU
94 performs frequency analysis (FFT) on both the detected pulse
wave data and the detected pressure data (detected body motion
data) stored in the RAM 95. Therefore, the MPU 94 essentially
constitutes a first frequency analyzing section and a second
frequency analyzing section.
[0257] Next, the MPU 94 determines the differential data, which is
the difference between the detected pulse wave data after analyzed
for frequency and the detected pressure data after analyzed for
frequency. The harmonic components of the pulse wave are then
extracted from the resulting differential data, and the pulse rate
is calculated from the frequency thereof.
[0258] A more specific pulse rate calculation process will now be
described.
[0259] FIG. 50 is an explanatory diagram of the frequency analysis
results for detected pulse wave data. FIG. 51 is an explanatory
diagram of the frequency analysis results for detected pressure
data.
[0260] First, the MPU 94 sequentially reads out the detected pulse
wave data and the detected pressure data stored in the RAM 95, and
subjects them to FFT for frequency analysis.
[0261] FIG. 52 is an explanatory diagram of differential data,
which is the difference between the detected pulse wave data after
analyzed for frequency and the detected pressure data after
analyzed for frequency.
[0262] Next, the MPU 94 compares the detected pulse wave data after
analyzed for frequency with the detected pressure data after
analyzed for frequency, and determines the difference between these
frequency components to create the differential data.
[0263] Thus, the frequency analysis results as the differential
data have the body motion components originating in the veins
substantially removed from the output signal (pulse wave
components+body motion components) of the pulse wave sensor, and
are, specifically, pulse wave data that primarily corresponds to
the pulse wave components.
[0264] Furthermore, the MPU 94 calculates the pulse rate from the
frequency on the assumption that the maximum frequency components
of the resulting pulse wave data constitute the pulse spectrum.
[0265] The MPU 94 then displays the pulse rate on the display
device 97.
[0266] As described above, according to the first alternative of
the second embodiment, variation in the veins, which is the main
factor in the body motion components generated in the body, can be
also accurately detected and registered. Therefore, the body motion
components can be accurately removed, making it possible to
accurately detect the pulse wave components, and hence to
accurately measure the pulse rate.
[0267] (2.2) Second Alternative of the Second Embodiment
[0268] A second alternative of the second embodiment is similar to
the second embodiment, except that the second embodiment has a
configuration in which the differential data is calculated by
subtracting the detected pressure data from the detected pulse wave
data prior to frequency analysis (FFT), while in the second
alternative, the MPU 94 is configured with an adaptive filter 100
and the body motion components are removed from the detected pulse
wave data. Therefore, the second alternative of the second
embodiment has the same configuration, except that the MPU 94 of
the pulse measurement device 80 of the second embodiment is
configured with an adaptive filter 100.
[0269] FIG. 53 shows a schematic structural block diagram of one
example of the adaptive filter 100. In general terms, the adaptive
filter 100 has a filter coefficient generating section 101 and a
synthesizer 102.
[0270] The filter coefficient generating section 101 functions as a
body motion component removing section and generates the adaptive
filter coefficient h based on data previously outputted by the
synthesizer 102 to which the filter has been applied. The adaptive
filter coefficient h is then applied to the detected pressure data
(=k(n)), which functions as the inputted body motion component
detection signal; body motion removal data (=h.multidot.k(n)) is
generated; and this data is outputted to the synthesizer 102.
[0271] The synthesizer 102 functions as a removal processing
section, combines the extracted detected pulse wave data (=pulse
wave components+body motion components) and the body motion removal
data, substantially removes (subtracts) the body motion components
contained in the current detected pulse wave data, and extracts
pulse wave components.
[0272] A more specific pulse rate calculation process according to
the second alternative of the second embodiment will now be
described.
[0273] FIG. 54 is a graph of a chronological arrangement of an
example of the detected pulse wave data. FIG. 55 is a graph in
which detected pressure data correlated with the detected pulse
wave data in FIG. 54 is chronologically arranged along the same
time axis.
[0274] First, the MPU 94 sequentially reads out the detected pulse
wave data and the detected pressure data stored in the RAM 95, and
outputs the detected pulse wave data in a certain sampling period
to the synthesizer 102.
[0275] Also, the MPU 94 presents the filter coefficient generating
section 101 with detected pressure data that corresponds to the
detected pulse wave data outputted to the synthesizer 102.
[0276] Thus, the filter coefficient generating section 101 creates
an adaptive filter coefficient h based on the data previously
outputted from the synthesizer 102 to which the adaptive filter has
been applied. The adaptive filter coefficient h is then applied to
the detected pressure data (=k(n)) functioning as the inputted body
motion component detection signal, and body motion removal data
(=h.multidot.k(n)) is outputted to the synthesizer 102.
[0277] Thus, the synthesizer 102 combines the current pulse wave
data and the body motion removal data, substantially removes
(subtracts) the body motion components contained in the current
detected pulse wave data, extracts the pulse wave components, and
outputs the residual data (=data to which the filter has been
applied).
[0278] FIG. 56 is a graph of a chronological arrangement of
residual data obtained by applying an adaptive filter to the
detected pulse wave data in FIG. 54 and the detected pressure data
in FIG. 55.
[0279] Next, the MPU 94 subjects the residual data to FFT.
[0280] FIG. 57 shows the frequency analysis results obtained by
subjecting the residual data in FIG. 56 to FFT.
[0281] Thus, the frequency analysis results thus obtained have the
body motion components originating in the veins substantially
removed from the output signal (pulse wave components+body motion
components) of the pulse wave sensor, and are, specifically, pulse
wave data that primarily corresponds to the pulse wave
components.
[0282] Furthermore, the MPU 94 calculates the pulse rate from the
frequency on the assumption that the maximum frequency components
of the resulting pulse wave data that primarily contains pulse wave
components constitute the pulse spectrum.
[0283] The MPU 94 then displays the pulse rate on the display
device 97.
[0284] As described above, according to the second alternative of
the second embodiment, variation in the veins, which is the main
factor in the body motion components generated in the body, can be
also accurately detected and registered. Therefore, the body motion
components can be accurately removed, making it possible to
accurately detect the pulse wave components, and hence to
accurately measure the pulse rate.
[0285] (2.3) Third Alternative of the Second Embodiment
[0286] A third alternative of the second embodiment is an
alternative in the sense that the sensor module 81 has both the
pulse wave sensor 83 and the pressure sensor 84 in the second
embodiment, while in the third alternative, the sensor module 81 is
divided into a sensor module 111A and a sensor module 111B, and the
pulse wave sensor 83 and pressure sensor 84 are mounted on separate
fingers. Aside from the above-mentioned differences, the
configuration of a pulse measurement device 110 in the third
alternative of the second embodiment is the same as the pulse
measurement device 80 of the second embodiment.
[0287] FIG. 58 is a schematic structural block diagram of a pulse
measurement system according to the third alternative of the second
embodiment. In general terms, the pulse measurement device 110 has
a sensor module 111A mounted on a first finger of the user, a
sensor module 111B mounted on a second finger of the user, and a
device main body 112 that is connected to the sensor module 111A
via a wiring L1, is also connected to the sensor module 111B via a
wiring L2, and is mounted on the arm of the user.
[0288] FIG. 59 is an explanatory diagram of the arrangement of
sensors in the sensor module 111 A. The sensor module 111A has the
pressure sensor 84 for primarily detecting body motion
components.
[0289] FIG. 60 is an explanatory diagram of the arrangement of the
sensors in the sensor module 111B. The sensor module 111B has the
pulse wave sensor 83 for primarily detecting pulse wave components.
As mentioned above, the pulse wave sensor 83 has the LED 83A for
emitting detection light, and the PD (Photo Detector) 83B for
receiving the detection light reflected by the body.
[0290] The actual detection operation is the same as in the second
embodiment described above, so a detailed description thereof is
omitted.
[0291] According to the third alternative of the second embodiment,
measurement is taken with the pressure sensor 84 for primarily
detecting body motion components and with the pulse wave sensor 83
for primarily detecting pulse wave components, mounted on separate
fingers, so it is possible to reduce the effect of the mechanical
arrangement of the other sensor and the effect of noise on the
output signal due to the output signal of the other sensor.
[0292] In the second embodiment and the first through third
alternatives of the second embodiment described above, the pressure
sensor 84 was provided either adjacent to or separate from the
pulse wave sensor 83, but it is also possible to use a
configuration in which the pressure sensor 84 is disposed in a
substantially layered state over the pulse wave sensor 83 in a
direction away from the body.
(3) Third Embodiment
[0293] A pulse measurement device 120 according to a third
embodiment of the present invention will now be described with
reference to FIGS. 61 through 81. The main difference between the
third embodiment and the second embodiment is that in the second
embodiment, venous blood pressure is detected using the pressure
sensor 84, while in the third embodiment, venous blood pressure is
estimated by detecting the relative difference in the vertical
direction between the position of the heart of the user and the
mounted position of the pulse meter with the aid of an angle sensor
122. Otherwise the basic configuration is similar to the first
embodiment or the second embodiment, therefore, in view of the
similarity between the first/second embodiment and the third
embodiment, descriptions of the parts of the third embodiment with
identical or similar functions to the parts of the first/second
embodiment are omitted for the sake of simplicity.
[0294] First, the operating principle of the third embodiment will
be described prior to a detailed description of the third
embodiment.
[0295] The second embodiment is configured to detect venous blood
pressure with a pressure sensor in order to detect body motion
components originating in venous blood. However, the third
embodiment focuses on the concept that the relative difference in
the vertical direction between the position of the heart of the
user and the mounted position of the pulse meter has a proportional
relationship with the vein meter pressure. Specifically, the third
embodiment is designed for a case in which the relative difference
in the vertical direction between the position of the heart of the
user and the mounted position of the pulse meter is detected as an
angle about the shoulder joint of the arm on which the pulse meter
is mounted (for example, 0.degree. when the arm hangs straight
down, and 90.degree. when the arm is horizontal).
[0296] Accordingly, the inventors have researched the relationship
between the amount of change in height (of the arm) and the amount
of body motion components (amount of stroke components) included in
the pulse wave sensor output when the same body motion components
are generated.
[0297] FIG. 61 is an explanatory diagram of the relationship
between the amount of change in height of the arm and the amount of
body motion components (amount of stroke components) included in
the pulse wave sensor output. As shown in FIG. 61, it is clear that
the amount of change in height of the arm and the amount of body
motion components (amount of stroke components) included in the
pulse wave sensor output have a substantially proportional
relationship. In other words, it is possible to surmise the effect
of venous blood included in the pulse wave sensor output if the
amount of change in height of the arm can be detected.
[0298] FIG. 62 is an explanatory diagram of the relationship
between the angle and direction of the arm. In the third
embodiment, the angle of the arm is 0.degree. and the direction is
down when the arm hangs straight down, the angle of the arm is
90.degree. and the direction is middle when the arm is horizontal,
and the angle of the arm is 180.degree. and the direction is up
when the arm is extended straight up. Also, the direction is
slanted down when the arm is intermediate between the position of
the arm hanging straight down and the position of the arm being
horizontal, and the direction is slanted up when the arm is
intermediate between the position of the arm being horizontal and
the position of the arm extending straight up.
[0299] FIG. 63 is an explanatory diagram of the relationship
between the amount of change in height of the arm position
(direction of the arm) in its initial state and the amount of body
motion components (amount of stroke components) as an angle sensor
output. As shown in FIG. 63, it is clear that when the position of
the vertical direction of the arm in its initial state is level
with or lower than the position of the heart of the user, or,
specifically, when the direction of the arm is between the down and
middle directions, the change in the amount of body motion
components (amount of stroke components), which is the output of
the angle sensor, displays the same tendency with any direction of
the arm even if the height of the position of the arm is varied. On
the other hand, when the position of the vertical direction of the
arm in its initial state is higher than the position of the heart
of the user, or, specifically, when the direction of the arm is
between the slanted up and up directions, it is clear that the
amount of body motion components (amount of stroke components) as
the angle sensor output has an overall tendency to decrease along
with a reduction in venous blood pressure.
[0300] FIG. 64 is an explanatory diagram of the change in the
amount of body motion components (stroke components) as the angle
sensor output due to the position of the arm when the amount of
change in height is fixed. As seen in FIG. 64, it is clear that the
amount of body motion components as the angle sensor output is low
when the angle of the arm is greater than 90.degree..
[0301] From these results, the angle sensor output shall be
corrected when the angle of the arm is greater than 90.degree..
[0302] FIG. 65 is an explanatory diagram of the relationship
between the amount of change in height of the position of the arm
(direction of the arm) in its initial state and the amount of body
motion components (stroke components) included in the angle sensor
output after correction. This case involves the example in FIG. 63,
in which the amount of body motion components (amount of stroke
components) Y corresponding to the angle sensor output is corrected
by the angle X of the arm according to the following formula when
the angle of the arm is greater than 90.degree..
Y=y.multidot.(X-90)/22.2, where
[0303] y is the amount of change in height (mV),
[0304] X is the angle (degree), and
[0305] Y is the amount of change in height (mV) after
correction.
[0306] As a result, as shown in FIG. 65, it is possible to detect
the amount of body motion components (amount of stroke components)
included in the pulse wave sensor output without any influence from
the arm position.
[0307] In view of this, in the third embodiment, the relative
difference in the vertical direction between the position of the
heart of the user and the mounted position of the pulse meter is
detected by an external angle sensor, and the body motion
components originating in the veins are subtracted from the pulse
wave sensor output at a specific rate, whereby the pulse rate is
accurately detected based on a signal from which the effect of
venous blood has been removed.
[0308] The third embodiment will now be described in detail.
[0309] FIG. 66A is a cross-sectional view of the pulse measurement
device 120 wherein the pulse meter of the third embodiment is
incorporated into a watchcase. In this example, the pulse wave
sensor 83 and an angle sensor 122 are provided on the reverse
surface of a watchcase 121 of the pulse measurement device 120. As
shown in FIG. 66A, the pulse wave sensor 83 described above is
formed integrally with the main body on the reverse side of the
watchcase 121. The watchcase 121 is provided with a wristband 123
for arm mounting, and the reverse side of the watchcase 121 is
pressed against the back of the wrist when the wristband 123 is
mounted by being wound around the wrist.
[0310] The reverse side of the watchcase 121 is provided with
transparent glass 83C as a component of the pulse wave sensor 83.
The transparent glass 83C is fixed to the watchcase 121 by a back
lid 124. The transparent glass 83C protects the LED 83A and the PD
83B, which are components of the pulse wave sensor 83, and also
transmits the light incident on the LED 13C and reflected light
obtained via the body, and directs the light to the PD 83B.
[0311] The front side of the watchcase 121 is provided with a
liquid crystal display device or another such display device 97 for
displaying the pulse rate HR and other such living organism
information based on the detection results from the pulse wave
sensor 83 in addition to the current time and date. Also, the
interior of the watchcase 121 has a CPU and other such IC circuits
on a main board 126, whereby a data processing circuit 127 is
configured.
[0312] Also, the reverse side of the main board 126 is provided
with a battery 128, and the battery 128 supplies power to the
display device 97, the main board 126, and the pulse wave sensor
83.
[0313] The main board 126 and the pulse wave sensor 83 are designed
to be connected by a heat seal 129, wherein power is supplied to
the pulse wave sensor 83 and the angle sensor 122 from the main
board 126, the pulse wave detection signal is fed to the main board
126 from the pulse wave sensor 83, and the angle detection signal
is fed from the angle sensor 122 by a wiring formed by the heat
seal 129.
[0314] The data processing circuit 127 subjects the pulse wave
signal to FFT and calculates the pulse rate HR by analyzing the
processing results thereof. The external surface of the watchcase
121 is provided with button switches (not shown) for time setting,
display mode switching, and the like.
[0315] The reverse side of the watchcase 121 faces the back of the
wrist when the wristband 123 is wound around the wrist. Therefore,
the light from the LED 83A is directed to the back of the wrist via
the transparent glass 83C, and the reflected light is received by
the PD 83B.
[0316] The angle sensor 122 outputs an angle detection signal used
to determine the relative difference in the vertical direction
between the position of the heart of the user and the mounted
position of the pulse meter. Therefore, the angle sensor 122
essentially constitutes a difference detecting section. A
differential capacitive sensor 122A or a rotary-spindle angle
sensor 122B is preferably used as the angle sensor 122.
[0317] FIG. 67 is a structural schematic diagram of the
differential capacitive sensor 122A used as the angle sensor. FIG.
68 is a partial enlarged diagram of the differential capacitive
sensor 122A before acceleration is applied.
[0318] The differential capacitive sensor 122A is a biaxial
acceleration sensor and has a first sensitivity axis LX1 and a
second sensitivity axis LX2. The differential capacitive sensor
122A has flexible tethers 132 supported by a pair of fixed shafts
131. The tethers 132 support a beam 133 from both sides. The beam
133 is provided with an electrode 133A protruding from the side,
which is held by a pair of fixed external electrodes 134A and 134B
so as to face both fixed external electrodes 134 at a position
virtually equidistant from the fixed external electrodes 134A and
134B. Thus, the electrode 133A and the fixed external electrodes
134A and 134B each function as capacitors with roughly the same
capacitance.
[0319] FIG. 69 is a partial enlarged diagram of a differential
capacitive sensor to which acceleration has been applied. In the
state shown in FIG. 68, when the differential capacitive sensor
122A is tilted, the tethers 132 bend due to gravitational
acceleration, resulting in the state shown in FIG. 69. As a result,
for example, the distance G1 between the electrode 133A and the
fixed external electrode 134A becomes greater than the distance G2
between the electrode 133A and the fixed external electrode 134B,
as shown in FIG. 69. Specifically, the capacitance of the capacitor
configured by the electrode 133A and the fixed external electrode
134B becomes greater. Therefore, since this difference in
capacitance is proportional to the extent of gravitational
acceleration, or, specifically, to the angle of inclination, it is
possible to detect the angle by measuring the difference in
capacitance.
[0320] FIG. 70 is a front view of the rotary-spindle angle sensor
122B used as the angle sensor. FIG. 71 is a side view of the
rotary-spindle angle sensor 122B in FIG. 70.
[0321] In general terms, the rotary-spindle angle sensor 122B has a
supporting axle 141, a rotary spindle 142 rotatably supported by
the supporting axle 141, a slitted plate 143 that rotates uniformly
with the rotary spindle 142 and has two groups of slits formed with
different phases, a fixed plate 144 for holding the supporting axle
141, and an optical sensor unit 145 disposed in a position on the
fixed plate 144 facing the slitted plate 143. According to this
configuration, the optical sensor unit 145 outputs an angle
detection signal with a pulse rate corresponding to the amount of
rotations of the slitted plate 143 for each group of slits when the
rotary spindle 142 rotates due to a change in the angle. At this
point it is also possible to detect the changing direction of the
angle because the phase relationship of the angle detection signals
for both groups of slits differs in terms of the rotating direction
of the rotary spindle.
[0322] The specific pulse rate calculation process in the third
embodiment will now be described.
[0323] FIG. 72 is a graph of a chronological arrangement of one
example of detected pulse wave data. FIG. 73 shows the frequency
analysis results obtained by subjecting the detected pulse wave
data in FIG. 72 to FFT. FIG. 74 is a graph of a chronological
arrangement of one example of detected angle data. FIG. 75 shows
the frequency analysis results obtained by subjecting the detected
angle data in FIG. 74 to FFT.
[0324] The configuration as a pulse measurement device is
essentially the same as the second embodiment, and will now be
described with reference to the schematic structural block diagram
in FIG. 66B. In this case, the body motion sensor 122 is an angle
sensor.
[0325] The MPU 94 has the functions of the adaptive filter 100'
shown in FIG. 66C.
[0326] First, the MPU 94 sequentially reads out the detected pulse
wave data and the detected angle data stored in the RAM 95, and
outputs the detected pulse wave data in a certain sampling period
to the synthesizer 102.
[0327] The MPU 94 also presents the filter coefficient generating
section 101 with detected angle data that corresponds to the
detected pulse wave data.
[0328] Thus, the filter coefficient generating section 101
generates the adaptive filter coefficient h based on the data
previously outputted by the synthesizer 102 to which the filter has
been applied. The adaptive filter coefficient h is then applied to
the inputted detected angle data (=k(n)) functioning as a body
motion component detection signal, and body motion removal data
(=h.multidot.k(n)) is outputted to the synthesizer 102.
[0329] The synthesizer 102 thereby combines the current pulse wave
data and body motion removal data, substantially removes
(subtracts) the body motion components contained in the current
detected pulse wave data, extracts the pulse wave components, and
outputs the residual data (=data to which the filter has been
applied).
[0330] FIG. 76 is a graph of a chronological arrangement of
residual data obtained by applying an adaptive filter to the
detected pulse wave data in FIG. 72 and the detected angle data in
FIG. 74.
[0331] Next, the MPU 94 subjects the residual data in FIG. 76 to
FFT.
[0332] FIG. 77 shows the frequency analysis obtained by subjecting
the residual data in FIG. 76 to FFT. Thus, the frequency analysis
results thus obtained have the body motion components originating
in the veins substantially removed from the output signal (pulse
wave components+body motion components) of the pulse wave sensor,
and are, specifically, pulse wave data that primarily corresponds
to the pulse wave components.
[0333] Furthermore, the MPU 94 calculates the pulse rate from the
frequency on the assumption that the maximum frequency components
of the resulting pulse wave data that primarily contains pulse wave
components constitute the pulse spectrum SP1.
[0334] The MPU 94 then displays the pulse rate on the display
device 97.
[0335] The above description pertained to a case in which the
output from the angle sensor 122 was not corrected, but as
described above, the body motion components as an output from the
angle sensor 122 is detected small when the angle of the arm is
greater than 90.degree.. Therefore, the output from the pulse wave
sensor 83 is corrected when the angle of the arm is greater than
90.degree.. FIG. 78 is a graph of a chronological arrangement of
one example of corrected detected angle data. FIG. 79 shows the
frequency analysis obtained by subjecting the corrected detected
angle data in FIG. 78 to FFT.
[0336] Similarly, the MPU 94 sequentially reads out the detected
pulse wave data and the detected angle data stored in the RAM 95,
outputs the detected pulse wave data in a certain sampling period
to the synthesizer 102, and outputs the corrected detected angle
data that corresponds to the detected pulse wave data to the filter
coefficient generating section 101.
[0337] Thus, the filter coefficient generating section 101 creates
the adaptive filter coefficient h based on the data previously
outputted by the synthesizer 102 to which the filter has been
applied. The adaptive filter coefficient h is then applied to the
inputted detected angle data functioning as a body motion component
detection signal and the body motion removal data
(=h.multidot.k(n)) is outputted to the synthesizer 102. The
synthesizer 102 then combines the current pulse wave data and body
motion removal data, substantially removes (subtracts) the body
motion components contained in the current detected pulse wave
data, extracts the pulse wave components, and outputs the residual
data (=data to which the filter has been applied).
[0338] FIG. 80 is a graph of a chronological arrangement of
residual data obtained by applying an adaptive filter to the
detected pulse wave data in FIG. 72 and the corrected detected
angle data in FIG. 78. The MPU 94 subjects this residual data to
FFT.
[0339] FIG. 81 shows the frequency analysis results obtained by
subjecting the residual data in FIG. 80 to FFT. As shown in FIG.
81, it is clear from the frequency analysis results thus obtained
that although the frequency analysis results and the height of the
peak on the pulse spectrum SP1 shown in FIG. 77 do not change, the
height of the peaks of other spectra is suppressed, and the MPU 94
can more accurately calculate the pulse rate from the frequency on
the assumption that the maximum frequency components of the pulse
wave data constitute the pulse spectrum SP1.
[0340] As described above, according to the third embodiment,
variation in the veins, which is the main factor in the body motion
components generated in the body, can be more accurately detected
and registered, particularly when angle correction is performed.
Therefore, the body motion components can be accurately removed,
making it possible to accurately detect pulse wave components, and
hence to accurately measure the pulse rate.
[0341] In the third embodiment described above, the angle sensor
122 was provided adjacent to or separate from the pulse wave sensor
83, but it is also possible to use a configuration in which the
angle sensor 122 is disposed in a substantially layered state over
the pulse wave sensor 83 in a direction away from the body.
[0342] Furthermore, the third embodiment described a case in which
the control program is stored in the ROM 96 in advance, but another
possibility is a configuration in which the control program is
stored in advance on various magnetic disks, optical disks, memory
cards, and other such storage media, and is read from these storage
media and installed. Another possibility is a configuration in
which a communication interface is provided for downloading the
control program via the Internet, LAN, or another such network;
installing the program; and running this program.
(4) Fourth Embodiment
[0343] A pulse measurement device 190 according to a fourth
embodiment of the present invention will now be described with
reference to FIGS. 82 through 109. The main difference between the
second embodiment and the fourth embodiment is that the fourth
embodiment uses a configuration in which, instead of using the
pressure sensor 84 of the second embodiment, body motion components
are estimated using a blood vessel simulation sensor 150, 160, 170,
or 180 for simulating the movement of venous blood, and these body
motion components are removed from the output signal of the pulse
wave sensor. Otherwise the basic configuration is similar to that
of the second embodiment; therefore, in view of the similarity
between the second embodiment and the fourth embodiment,
descriptions of the parts of the fourth embodiment with identical
or similar functions to the parts of the second embodiment may be
omitted for the sake of simplicity.
[0344] First, the operating principle of the fourth embodiment will
be described prior to a detailed description of the fourth
embodiment.
[0345] The output of the pulse wave sensor for detecting pulse
waves includes various body motion components in addition to pulse
wave components. These body motion components are known to be
generated by changes in the body originating in the movements
(walking/running, arm movement, and the like) of the user whose
pulse is to be measured, and, as described above, means in which
detection light from an LED, which is a light-emitting element, is
directed into the body, and reflected light is received by a PD
(Photo Detector), which is a light receiving element, is used as
the means for detecting the body motion components inside the
body.
[0346] In this case, the detection light directed into the body is
absorbed and scattered by arteriolovenous blood flowing near the
skin and by the body tissue, the change in the amount of detection
light received by the PD at rest in the absence of movement is
primarily determined by the change in arterial blood due to
pulsation, and absorbed light components due to venous blood and
tissues are substantially constant.
[0347] However, in addition to changes in arterial blood due to
pulsation, the movement of venous blood due to inertia and
deformation of tissues and other such variations are generated
synchronously with body motion during movement (walking, running,
and the like) that accompanies body motion. As a result, the
detection light directed to the inside of the body changes in terms
of its absorptive and reflective characteristics and is received in
the PD, and the effect thereof cannot be ignored.
[0348] On the other hand, when the sensor for detecting body motion
components is mounted on the body surface of the user in a
pressurized state by an elastic band (for example, a supporter),
the movement of venous blood is primarily detected under such
circumstances because variations in tissue and other such
fluctuations are suppressed.
[0349] In view of the foregoing, the fourth embodiment involves
estimating the body motion components by focusing on the movement
of venous blood and simulating the movement of venous blood when
the body motion components in the body are to be removed, and
removing the body motion components from the output signal of the
pulse wave sensor.
[0350] FIG. 82 is a diagram illustrating the principle of a blood
vessel simulation sensor mounted on the body and designed for
simulating the movement (behavior) of venous blood.
[0351] Compared to arterial blood, venous blood has low blood
pressure and is therefore susceptible to the effect of inertial
force due to gravity and arm movements. Therefore, as shown in FIG.
82, a solution LQ with a specific viscosity is sealed inside a
cylindrical sealed container that models a blood vessel in the
peripheral direction, whereby it is possible to estimate the body
motion (behavior) of venous blood by observing the body motion
(behavior) of the solution from the outside, and the body motion
components generated in the body can be observed from the estimated
movement of venous blood.
[0352] In the fourth embodiment, the movement of the solution
sealed inside a cylindrical sealed container is detected by a
pressure sensor, an optical sensor or another such sensor, and body
motion components generated in the body are detected based on the
output signal of this sensor.
[0353] As a result, according to the fourth embodiment, the pulse
rate is accurately detected based on the signal from which the
effect of venous blood has been removed.
[0354] The embodiments of the blood vessel simulation sensor will
now be described with reference to FIGS. 83 through 88. In general
terms, the embodiments of the blood vessel simulation sensor are
classed into a rigid type, an elastic type, and an acceleration
sensor type. The rigid type is a sensor in which a solution with a
viscosity (for example, 1 to 100 cP) that exhibits the same
behavior as blood is sealed in a rigid cylindrical container. The
elastic type is a sensor in which a resilient tube is closed off at
both ends and a solution with a viscosity (for example, 1 to 100
cP) that exhibits the same behavior as blood is sealed in the tube.
The blood vessel simulation sensor of the acceleration sensor type
is one in which the acceleration sensor in FIG. 82 whose direction
of sensitivity is the peripheral direction is used as a blood
vessel simulation sensor.
[0355] FIG. 83 is a schematic diagram of a first rigid type of
blood vessel simulation sensor 150. The blood vessel simulation
sensor 150 has a resinous (plastic) casing 151 closed off at both
ends, and simulation blood 152 whose viscosity is set to ensure a
behavior similar to that of venous blood is sealed in the casing
151 inside the sensor. Furthermore, a pressure sensor (behavior
detection sensor) 153 for detecting pressure changes in accordance
with the movement of the simulation blood 152 is provided to one
end of the casing 151 in the longitudinal direction.
[0356] FIG. 84 is a schematic diagram of a second rigid type of
blood vessel simulation sensor 160. The blood vessel simulation
sensor 160 has a resinous (plastic) casing 161 closed off at both
ends, and simulation blood 162 whose viscosity is set to ensure a
behavior similar to that of venous blood is sealed in the casing
161 inside the sensor. Furthermore, an optical sensor (behavior
detection sensor) 163 for detecting the state of movement of the
simulation blood 162 is provided to the sidewall of the casing 161.
The optical sensor 163 has an LED 164 for emitting detection light
and a PD 165 for receiving the detection light. In this case, the
simulation blood 162 is colored the same as the detection light,
and the optical sensor 163 detects changes in the state of the
liquid surface.
[0357] FIG. 85 is a schematic diagram of a first elastic type of
blood vessel simulation sensor 170. The blood vessel simulation
sensor 170 has a resinous (plastic) casing 171 closed off at both
ends, and simulation blood 172 whose viscosity is set to ensure a
behavior similar to that of venous blood is sealed in the casing
171 inside the sensor. Furthermore, a pressure sensor (behavior
detection sensor) 173 for detecting pressure changes in accordance
with the movement of the simulation blood 172 is provided to one
end of the casing 171 in the longitudinal direction.
[0358] FIG. 86 is a schematic diagram of a second elastic type of
blood vessel simulation sensor 180. The blood vessel simulation
sensor 180 has a resilient resinous casing 181 made of rubber or
the like and closed off at both ends, and simulation blood 182
whose viscosity is set to ensure a behavior similar to that of
venous blood is sealed in the casing 181 inside the sensor.
Furthermore, a pressure sensor (behavior detection sensor) 183 for
detecting pressure changes in accordance with the movement of the
simulation blood 182 is provided to the sidewall of the casing
181.
[0359] The relationship between the rigid type and elastic type of
blood vessel simulation sensors 150 through 180 and the body motion
components (stroke components) detected by the separate pulse wave
sensors will now be described.
[0360] FIG. 87 is an explanatory diagram of the relationship
between the rigid type of blood vessel simulation sensor 150 or 160
and the body motion components (stroke components) included in the
output of the pulse wave sensor 83. As shown in FIG. 87, it is
clear that the output of the rigid type of blood vessel simulation
sensor 150 or 160 has a substantially proportional correlation with
the size of the body motion components (stroke components) included
in the output of the pulse wave sensor 83.
[0361] FIG. 88 is an explanatory diagram of the relationship
between the elastic type of blood vessel simulation sensor 170 or
180 and the body motion components (stroke components) included in
the output of the pulse sensor 83. As shown in FIG. 88, it is clear
that the output of the elastic type of blood vessel simulation
sensor 170 or 180 has a substantially proportional correlation with
the size of the body motion components (stroke components) included
in the output of the pulse wave sensor 83, similar to the output of
the rigid type of blood vessel simulation sensor 150 or 160.
[0362] Therefore, it is clear that when the body motion components
(stroke components) included in the output signal of the pulse wave
sensor 83 are assumed to be primarily determined by the movement of
venous blood, it is possible to estimate the amount of body motion
components contained in the output signal of the pulse wave sensor
using any of the blood vessel simulation sensors 150 through 180 or
an acceleration sensor type of blood vessel simulation sensor.
[0363] The fourth embodiment will now be described in detail. FIG.
89 is a schematic structural block diagram of a pulse measurement
device 190 of the fourth embodiment.
[0364] In general terms, the pulse measurement device 190 has a
sensor module 191 mounted on the finger of the user, and a device
main body 192 connected to the sensor module 191 via a wiring LN
and mounted on the arm of the user.
[0365] FIG. 90 is an explanatory diagram of the arrangement of the
sensors in the sensor module in a mounted state. In general terms,
the sensor module 191 is configured with a pulse wave sensor 83 for
primarily detecting pulse wave components and a blood vessel
simulation sensor described above for primarily detecting body
motion components. In the fourth embodiment, the first rigid type
of blood vessel simulation sensor 150 is used as the blood vessel
simulation sensor. In this case, the first rigid type of blood
vessel simulation sensor 150 is disposed near the pulse wave sensor
83 and is also disposed in a substantially layered state over the
pulse wave sensor 83 in a direction away from the user (the body).
The pulse wave sensor 83 referred to herein has an LED 83A for
emitting detection light and a PD 83B for receiving the detection
light reflected by the body.
[0366] FIG. 91 is a schematic structural block diagram of the pulse
measurement device 190. In general terms, the pulse measurement
device 190 has, in addition to the pulse wave sensor 83 described
above, a blood vessel simulation sensor 150 as a body motion
sensor, a pulse wave signal amplifying circuit 91, a body motion
signal amplifying circuit 92, an A/D conversion circuit 93, an MPU
94, a RAM 95, a ROM 96, and a display device 97.
[0367] The pulse wave signal amplifying circuit 91 amplifies the
pulse wave detection signal outputted from the pulse wave sensor 83
at a prescribed rate of amplification, and outputs the result as an
amplified pulse wave detection signal to the A/D conversion circuit
93.
[0368] The body motion signal amplifying circuit 92 amplifies the
pressure detection signal based on the movement of the simulation
blood 152 and outputted from the first rigid type of blood vessel
simulation sensor 150 functioning as a body motion sensor at a
specific rate, and outputs the result as an amplified pressure
detection signal to the A/D conversion circuit 93.
[0369] The A/D conversion circuit 93 performs analog/digital
conversion separately on the inputted amplified pulse wave
detection signal and the amplified pressure detection signal, and
outputs the result as detected pulse wave data and detected
pressure data to the MPU 94.
[0370] The MPU 94 stores the detected pulse wave data and detected
pressure data (detected body motion data) for the pressure
detection signal outputted from the first rigid type of blood
vessel simulation sensor 150 in the RAM 95, calculates the pulse
rate based on a control program stored in the ROM 96, and displays
the result on the display device 97.
[0371] More specifically, the MPU 94 chronologically arranges the
detected pulse wave data and the detected pressure data (detected
body motion data) stored in the RAM 95 and determines the
differential data, which is the difference between the detected
pulse wave data and the detected pressure data for each
corresponding sampling time.
[0372] Frequency analysis (FFT: Fast Fourier Transformation) is
then performed on the differential data, the harmonic components of
the pulse wave are extracted, and the pulse rate is calculated from
the frequency.
[0373] The specific pulse rate calculation process will now be
described.
[0374] FIG. 92 is a graph of a chronological arrangement of one
example of the detected pulse wave data. FIG. 93 is a graph in
which detected pressure data correlated with the detected pulse
wave data in FIG. 92 is chronologically arranged along the same
time axis.
[0375] First, the MPU 94 sequentially reads out the detected pulse
wave data and the detected pressure data stored in the RAM 95 and
calculates the differential data by subtracting the detected
pressure data in a certain sampling period from the detected pulse
wave data at the same sampling timing.
[0376] FIG. 94 is a graph of a chronological arrangement of
differential data calculated from the detected pulse wave data in
FIG. 92 and the detected pressure data in FIG. 93.
[0377] Next, the MPU 94 subjects the differential data to FFT.
[0378] FIG. 95 shows the frequency analysis results obtained by
subjecting the differential data in FIG. 94 to FFT.
[0379] Thus, the frequency analysis results thus obtained have the
body motion components originating in the veins substantially
removed from the output signal (pulse wave components+body motion
components) of the pulse wave sensor 83, and are, specifically,
pulse wave data that primarily corresponds to the pulse wave
components.
[0380] Furthermore, the MPU 94 calculates the pulse rate from the
frequency on the assumption that the maximum frequency components
of the resulting pulse wave data constitute the pulse spectrum
PH1.
[0381] The MPU 94 then displays the pulse rate on the display
device 97.
[0382] As described above, according to the fourth embodiment,
variation in the veins, which is the main factor in the body motion
components generated in the body, can be accurately estimated based
on the output signal from the blood vessel simulation sensor.
Therefore, the body motion components can be accurately removed,
making it possible to accurately detect pulse wave components, and
hence to accurately measure the pulse rate.
[0383] The fourth embodiment describes the first rigid type of
blood vessel simulation sensor 150 used as the rigid type of blood
vessel simulation sensor, but the second rigid type of blood vessel
simulation sensor 160 may also be used.
[0384] (4.1) First Alternative of the fourth Embodiment
[0385] A first alternative of the fourth embodiment is similar to
the fourth embodiment except that the fourth embodiment uses a
configuration in which differential data is calculated by
subtracting detected pressure data, which corresponds to the
pressure detection signal outputted from the first rigid type of
blood vessel simulation sensor 150, from the detected pulse wave
data prior to frequency analysis (FFT), while the first alternative
uses a configuration in which the differential data is calculated
after frequency analysis is performed on the detected pulse wave
data and on the detected pressure data that corresponds to the
pressure detection signal outputted from the first rigid type of
blood vessel simulation sensor 150.
[0386] In the first alternative of the fourth embodiment, the MPU
94 performs frequency analysis (FFT) separately on the detected
pulse wave data and the detected pressure data (detected body
motion data) that corresponds to the pressure detection signal
outputted from the first rigid type of blood vessel simulation
sensor 150 stored in the RAM 95.
[0387] Next, the MPU 94 determines the differential data, which is
the difference between the detected pulse wave data after analyzed
for frequency and the detected pressure data after analyzed for
frequency. The harmonic components of the pulse wave are then
extracted from the resulting differential data, and the pulse rate
is calculated from the frequency thereof.
[0388] A more specific pulse rate calculation process will now be
described.
[0389] FIG. 96 is an explanatory diagram of the frequency analysis
results for detected pulse wave data.
[0390] FIG. 97 is an explanatory diagram of the frequency analysis
results for detected pressure data that corresponds to the pressure
detection signal outputted from the first rigid type of blood
vessel simulation sensor 150.
[0391] First, the MPU 94 sequentially reads out the detected pulse
wave data and the detected pressure data stored in the RAM 95, and
subjects them to FFT.
[0392] FIG. 98 is an explanatory diagram of differential data,
which is the difference between the detected pulse wave data after
analyzed for frequency and the detected pressure data after
analyzed for frequency.
[0393] Next, the MPU 94 compares the detected pulse wave data after
analyzed for frequency with the detected pressure data after
analyzed for frequency, and determines the difference between these
frequency components to create the differential data.
[0394] Thus, the frequency analysis results thus obtained as the
differential data have the body motion components originating in
the veins substantially removed from the output signal (pulse wave
components+body motion components) of the pulse wave sensor, and
are, specifically, pulse wave data that primarily corresponds to
the pulse wave components.
[0395] Furthermore, the MPU 94 calculates the pulse rate from the
frequency on the assumption that the maximum frequency components
of the resulting pulse wave data constitute the pulse spectrum
PH1.
[0396] The MPU 94 then displays the pulse rate on the display
device 97.
[0397] As described above, according to the first alternative of
the fourth embodiment, variation in the veins, which is the main
factor of the body motion components generated in the body, can be
surely estimated with a blood vessel simulation sensor. Therefore,
the body motion components can be surely removed, making it
possible to accurately detect pulse wave components, and hence to
accurately measure the pulse rate.
[0398] (4.2) Second Alternative of the Fourth Embodiment
[0399] A second alternative of the fourth embodiment is similar to
the fourth embodiment except that the fourth embodiment uses a
configuration in which differential data is calculated by
subtracting detected pressure data, which corresponds to the
pressure detection signal outputted from the first rigid type of
blood vessel simulation sensor 150, from the detected pulse wave
data prior to frequency analysis (FFT), while the second
alternative uses a configuration in which the MPU 94 has an
adaptive filter 200, and the body motion components that correspond
to the pressure detection signal outputted from the blood vessel
simulation sensor 150 are removed from the detected pulse wave
data.
[0400] FIG. 99 is a schematic structural block diagram of one
example of the adaptive filter 200. In general terms, the adaptive
filter 200 has a filter coefficient generating section 201 and a
synthesizer 202.
[0401] The filter coefficient generating section 201 functions as a
body motion component removing section and generates the adaptive
filter coefficient h based on data previously outputted by the
synthesizer 202 to which the filter has been applied. The adaptive
filter coefficient h is then applied to the detected pressure data
(=k(n)), which functions as the body motion component detection
signal inputted from the blood vessel simulation sensor; body
motion removal data (=h.multidot.k(n)) is generated; and this data
is outputted to the synthesizer 202.
[0402] The synthesizer 202 functions as a removal processing
section, combines the extracted detected pulse wave data (=pulse
wave components+body motion components) and the body motion removal
data, substantially removes (subtracts) the body motion components
contained in the current detected pulse wave data, and extracts
pulse wave components.
[0403] A more specific pulse rate calculation process according to
the second alternative will now be described.
[0404] FIG. 100 is a graph of a chronological arrangement of an
example of the detected pulse wave data. FIG. 101 is a graph in
which the detected pressure data inputted from the blood vessel
simulation sensor and correlated with the detected pulse wave data
in FIG. 100 is chronologically arranged along the same time
axis.
[0405] First, the MPU 94 sequentially reads out the detected pulse
wave data and the detected pressure data stored in the RAM 95, and
outputs the detected pulse wave data in a certain sampling period
to the synthesizer 202.
[0406] Also, the MPU 94 presents the filter coefficient generating
section 201 with detected pressure data that corresponds to the
detected pulse wave data.
[0407] Thus, the filter coefficient generating section 201 creates
an adaptive filter coefficient h based on the data previously
outputted from the synthesizer 202 to which the adaptive filter has
been applied. The adaptive filter coefficient h is then applied to
the detected pressure data (=k(n)) functioning as the body motion
component detection signal inputted from a result simulation
sensor, and body motion removal data (=h.multidot.k(n)) is
outputted to the synthesizer 202.
[0408] Thus, the synthesizer 202 combines the current pulse wave
data and the body motion removal data, substantially removes
(subtracts) the body motion components contained in the current
detected pulse wave data, extracts the pulse wave components, and
outputs the residual data (=data to which the filter has been
applied).
[0409] FIG. 102 is a graph of a chronological arrangement of
residual data obtained by applying an adaptive filter to the
detected pulse wave data in FIG. 100 and the detected pressure data
outputted from the blood vessel simulation sensor in FIG. 101.
[0410] Next, the MPU 94 subjects the residual data to FFT.
[0411] FIG. 103 shows the frequency analysis results obtained by
subjecting the residual data in FIG. 102 to FFT.
[0412] Thus, the frequency analysis results thus obtained have the
body motion components originating in the veins, which are
estimated based on the blood vessel simulation sensor output,
substantially removed from the output signal (pulse wave
components+body motion components) of the pulse wave sensor, and
are, specifically, pulse wave data that primarily corresponds to
the pulse wave components.
[0413] Furthermore, the MPU 94 calculates the pulse rate from the
frequency on the assumption that the maximum frequency components
of the resulting pulse wave data that primarily contains pulse wave
components constitute the pulse spectrum.
[0414] The MPU 94 then displays the pulse rate on the display
device 97.
[0415] As described above, according to the second alternative of
the fourth embodiment, variation in the veins, which is the main
factor of the body motion components generated in the body, can be
surely estimated with a blood vessel simulation sensor, whereby the
body motion components can be accurately removed, making it
possible to surely detect pulse wave components, and hence to
accurately measure the pulse rate.
[0416] (4.3) Third Alternative of the Fourth Embodiment
[0417] A third alternative of the fourth embodiment will now be
described. The third alternative of the fourth embodiment is
similar to the fourth embodiment, except that the sensor module 191
having the rigid type of blood vessel simulation sensor 150 in the
fourth embodiment is replaced by a sensor module 191 A having a
resilient type of blood vessel simulation sensor 170.
[0418] FIG. 104A is an explanatory diagram of the arrangement of
sensors in the sensor module 191A in a mounted state. FIG. 104B is
a schematic structural block diagram of the pulse measurement
device according to the third alternative of the fourth
embodiment.
[0419] As shown in FIG. 104A, in general terms, the sensor module
191A is configured to include the pulse wave sensor 83 for
primarily detecting pulse wave components, and the first resilient
type of blood vessel simulation sensor 170 described above for
primarily detecting body motion components.
[0420] Such a configuration makes it possible to surely estimate
body motion components generated in the body and to remove the body
motion components in a more similar to the actual veins.
[0421] The third alternative of the fourth embodiment describes the
use of the first elastic type of blood vessel simulation sensor 170
as an elastic type of blood vessel simulation sensor, but a second
elastic type of blood vessel simulation sensor 180 may also be
used.
[0422] (4.4) Fourth Alternative of the Fourth Embodiment
[0423] A fourth alternative of the fourth embodiment will now be
described. The fourth alternative of the fourth embodiment is
similar to the fourth embodiment, except that the sensor module 191
having the rigid type of blood vessel simulation sensor 150 in the
fourth embodiment is replaced by a sensor module 191B having an
acceleration sensor 210 as a blood vessel simulation sensor.
[0424] FIG. 105A is an explanatory diagram of the arrangement of
sensors in the sensor module 191B in a mounted state. FIG. 105B is
a schematic structural block diagram of the pulse measurement
device according to the fourth alternative of the fourth
embodiment.
[0425] As shown in FIG. 105A, in general terms, the sensor module
191B is configured having a pulse wave sensor 83 for primarily
detecting pulse wave components, and the acceleration sensor 210
for primarily detecting acceleration in the peripheral direction
shown in FIG. 82.
[0426] In this case, the acceleration sensor 210 as the blood
vessel simulation sensor is disposed near the pulse wave sensor 83
and is also disposed in a substantially layered state over the
pulse wave sensor 83 in a direction away from the user (the
body).
[0427] The configuration of the acceleration sensor 210 will now be
examined in detail.
[0428] FIG. 106 is an explanatory diagram of the relationship
between acceleration in the direction of the X-axis described
hereinbelow when a triaxial (X, Y, Z-axes) acceleration sensor is
used as the acceleration sensor, and the body motion components
(stroke components) included in the output signal of the pulse wave
sensor 83.
[0429] FIG. 107 is an explanatory diagram of the relationship
between acceleration in the direction of the Y-axis described
hereinbelow when a triaxial acceleration sensor described
hereinbelow is used as the acceleration sensor, and the body motion
components (stroke components) included in the output signal of the
pulse wave sensor 83.
[0430] FIG. 108 is an explanatory diagram of the relationship
between acceleration in the direction of the Z-axis described
hereinbelow when a triaxial (X, Y, Z-axes) acceleration sensor
described hereinbelow is used as the acceleration sensor, and the
body motion components (stroke components) included in the output
signal of the pulse wave sensor 83.
[0431] FIG. 109 is an explanatory diagram of the three axes. As
shown in FIG. 109, the X-axis extends in the peripheral direction
(direction of the fingertips) shown in FIG. 82, the Y-axis is
perpendicular to and lies in the same plane as the X-axis when the
palm of the hand is aligned in this plane, and the Z-axis is
perpendicular to the plane containing the palm of the hand.
[0432] As shown in FIGS. 106 through 108, it is clear that the body
motion components contained in the output signal of the pulse wave
sensor 83 are primarily based on components in the X-axis
direction. Therefore, it is possible to estimate the body motion
components detected by the pulse wave sensor 83 if a uniaxial
acceleration sensor capable of detecting acceleration only in the
X-axis direction, or, specifically, in the peripheral direction
shown in FIG. 82, is used as the acceleration sensor 210.
(5) Fifth Embodiment
[0433] A pulse measurement device 220 according to a fifth
embodiment of the present invention will now be described with
reference to FIGS. 110 and 111. The main difference between the
fourth embodiment and the fifth embodiment is that in the fourth
embodiment, the pulse wave sensor 83 and the blood vessel
simulation sensor 150 are configured integrally as the sensor
module 191, while in the fifth embodiment, the blood vessel
simulation sensor 150 is incorporated into the main body of the
device. Otherwise the basic configuration is similar to the fourth
embodiment; therefore, in view of the similarity between the fourth
embodiment and the fifth embodiment, descriptions of the parts of
the fifth embodiment with identical or similar functions to the
parts of the fourth embodiment may be omitted for the sake of
simplicity.
[0434] FIG. 110 is an external perspective view of the pulse
measurement device 220 of the fifth embodiment. FIG. 111 is a
cross-sectional view of a sensor module 221 in FIG. 110.
[0435] In general terms, the pulse measurement device 220 has the
sensor module 221 mounted on the finger of the user, and a device
main body 222 connected to the sensor module 221 via a wiring LN
and mounted on the arm of the user.
[0436] As shown in FIG. 111, in general terms, the sensor module
221 is configured having a pulse wave sensor 83 for primarily
detecting pulse wave components.
[0437] The pulse wave sensor 83 has an LED 83A for emitting
detection light and a PD 83B for receiving the detection light
reflected by the body.
[0438] Also, as shown in FIG. 110, the blood vessel simulation
sensor 150 is accommodated in the device main body 222 in such a
state that the sensitivity axis virtually coincides with the
peripheral direction of the body (direction of the fingertips).
[0439] Since the specific operation of the fifth embodiment is
similar to the fourth embodiment, a detailed description is
omitted.
[0440] As described above, according to the fifth embodiment, in
addition to the effects of the fourth embodiment, finger movements
and other such small movements are not erroneously detected by the
blood vessel simulation sensor 150, the size of the sensor module
can be reduced, mounting is made easier, and the user's sensation
of wearing the device is improved because the blood vessel
simulation sensor 150 is incorporated into the main body of the
device.
[0441] A case of using the first rigid type of blood vessel
simulation sensor 150 as a body motion sensor was described above
as an example, but it is also possible to use the second rigid type
of blood vessel simulation sensor, the first elastic type of blood
vessel simulation sensor 170, the second resilient type of blood
vessel simulation sensor 180, or the acceleration sensor 210 as a
blood vessel simulation sensor for the body motion sensor instead
of the first rigid type of blood vessel simulation sensor 150. Also
in such cases, finger movements and other such small movements are
not erroneously detected, the size of the sensor module is reduced,
mounting is made easier, and the user's sensation of wearing the
device is improved by incorporating the sensor used as the body
motion sensor into the main body of the device.
(6) Sixth Embodiment
[0442] A pulse measurement device 230 according to a sixth
embodiment of the present invention will now be described with
reference to FIGS. 112 and 113. The main difference between the
fourth embodiment and the sixth embodiment is that in the fourth
embodiment, the sensor module 191 and the device main body 192 are
provided separately and are connected by wiring, while in the sixth
embodiment, the sensor module is incorporated into the main body of
the device. Otherwise the basic configuration is similar to the
fourth embodiment; therefore, in view of the similarity between the
fourth embodiment and the sixth embodiment, descriptions of the
parts of the sixth embodiment with identical or similar functions
to the parts of the fourth embodiment may be omitted for the sake
of simplicity.
[0443] FIG. 112 is an external perspective view of a case in which
the pulse measurement device 230 of the sixth embodiment is
incorporated in a watchcase. FIG. 113 is a cross-sectional view of
the pulse measurement device 230 in FIG. 112.
[0444] In this example, the pulse wave sensor 83 and a blood vessel
simulation sensor 232 are provided on the reverse surface of a
watchcase 231. As shown in FIG. 113, the pulse wave sensor unit 83
described above is formed integrally with the main body on the
reverse side of the watchcase 231. The watchcase 231 is provided
with a wristband 233 for mounting the watchcase 231 on the arm, and
the reverse side of the watchcase 231 is pressed against the back
of the wrist when the wristband 233 is wound around the wrist.
[0445] The transparent glass 83C constituting the pulse wave sensor
83 is fixed to the reverse side of the watchcase 231 by a back lid
234. In addition to protecting the LED 83A and the PD 83B of the
pulse wave sensor 83, the transparent glass 83C transmits the light
cast on the LED 83A, transmits reflected light obtained via the
body, and directs the light to the PD 83B. The front side of the
watchcase 231 is provided with a liquid crystal display device or
another such display device 97 for displaying the pulse rate HR and
other such living organism information based on the detection
results from the pulse wave sensor 83 in addition to the current
time and date. Also, the interior of the watchcase 231 has a CPU
and other such IC circuits on a main board 236, whereby a data
processing circuit 237 is configured.
[0446] Also, the reverse side of the main board 236 is provided
with a battery 238, and the battery 238 supplies power to the
display device 97, the main board 236, the pulse wave sensor 83,
and the blood vessel simulation sensor 232.
[0447] The main board 236 and the pulse wave sensor 83 are
connected by a heat seal 239, power is supplied from the main board
236 to the pulse wave sensor 83 through a wiring formed by the heat
seal 239, and a pulse wave detection signal is fed from the pulse
wave sensor 83 to the main board 236.
[0448] The data processing circuit 237 subjects the pulse wave
signal to FFT processing, and the pulse rate HR is calculated by
analyzing the processing results. The external surface of the
watchcase 231 is provided with button switches (not shown) for time
setting, display mode switching, and the like.
[0449] The reverse side of the watchcase 231 faces the back of the
wrist when the wristband 233 is wound around the wrist. Therefore,
the light from the LED 83A is directed to the back of the wrist via
the transparent glass 83C, and the reflected light is received by
the photo diode 83B.
[0450] Since the specific operation of the sixth embodiment is
similar to the fourth embodiment, a detailed description is
omitted.
[0451] As described above, according to the sixth embodiment, in
addition to the effects of the fourth embodiment, finger movements
and other such small movements are not erroneously detected and
mounting is made easier because the sensor module is incorporated
into the main body of the device.
[0452] A case of using the blood vessel simulation sensor 232 as a
body motion sensor was described above as an example, but it is
also possible to use the first rigid type of blood vessel
simulation sensor 150, a second rigid type of blood vessel
simulation sensor, the first resilient type of blood vessel
simulation sensor 170, the second resilient type of blood vessel
simulation sensor 180, or the acceleration sensor 210 as a blood
vessel simulation sensor for the body motion sensor instead of the
blood vessel simulation sensor 232. Also in such cases, finger
movements and other such small movements are not erroneously
detected and mounting is made easier by incorporating the sensor
used as the body motion sensor into the main body of the
device.
[0453] In the above descriptions of the first embodiment through
the sixth embodiment, a case of storing a control program in the
ROM 26 or the ROM 96 in advance was described, but another
possibility is a configuration in which the control program is
stored in advance on various magnetic disks, optical disks, memory
cards, and other such storage media, and is read from these storage
media and installed. Another possibility is a configuration in
which a communication interface is provided for downloading the
control program via the Internet, LAN, or another such network;
installing the program; and running this program.
[0454] The term "configured" as used herein to describe a
component, section or part of a device includes hardware and/or
software that is constructed and/or programmed to carry out the
desired function.
[0455] As used herein, the following directional terms "forward,
rearward, above, downward, vertical, horizontal, below and
transverse" as well as any other similar directional terms refer to
those directions of any pulse measurement device equipped with the
present invention. Accordingly, these terms, as utilized to
describe the present invention should be interpreted relative to
any pulse measurement device equipped with the present
invention.
[0456] The terms of degree such as "substantially", "about" and
"approximately" as used herein mean a reasonable amount of
deviation of the modified term such that the end result is not
significantly changed. For example, these terms can be construed as
including a deviation of at least .+-.5% of the modified term if
this deviation would not negate the meaning of the word it
modifies.
[0457] This specification claims priority to Japanese Patent
Application Nos. 2003-75839, 2003-75840, and 2003-310624. All of
the disclosures in Japanese Patent Application Nos. 2003-75839,
2003-75840, and 2003-310624 are incorporated herein by
reference.
[0458] While only selected embodiments have been chosen to
illustrate the present invention, it will be apparent to those
skilled in the art from this disclosure that various changes and
alternatives can be made herein without departing from the scope of
the invention as defined in the appended claims. Furthermore, the
foregoing descriptions of the embodiments according to the present
invention are provided for illustration only, and not for the
purpose of limiting the invention as defined by the appended claims
and their equivalents. Thus, the scope of the invention is not
limited to the disclosed embodiments.
* * * * *