U.S. patent application number 16/853262 was filed with the patent office on 2021-04-08 for vital signs monitoring system.
The applicant listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to ALBERTO GIOVANNI BONOMI, KOEN THEO JOHAN DE GROOT.
Application Number | 20210100500 16/853262 |
Document ID | / |
Family ID | 1000005277867 |
Filed Date | 2021-04-08 |
United States Patent
Application |
20210100500 |
Kind Code |
A1 |
DE GROOT; KOEN THEO JOHAN ;
et al. |
April 8, 2021 |
VITAL SIGNS MONITORING SYSTEM
Abstract
A vital signs monitoring system comprises a processing unit
(300) for estimating an activity energy expenditure (AEE), a first
activity energy expenditure determining unit (320) for determining
a first activity energy expenditure (AEEHR) based on heart rate
data (HR), a second activity energy expenditure determining unit
(330) for determining a second activity energy expenditure (AEEAC)
based on motion data (AC), and a weighting unit (340) for
determining a first and second weighting factor (wHR, wAC) based on
a first and second probability relating to a high exertion (hH) and
relating to a low exertion (hL), and activity energy expenditure
calculating unit (350) for computing an overall activity energy
expenditure (AEEO) based on the first activity energy expenditure
(AEEHR) weighted by the first weighting factor (wHR) and on the
second activity energy expenditure (AEEAC) weighted by the second
weighting factor (wAC).
Inventors: |
DE GROOT; KOEN THEO JOHAN;
(SEVENUM, NL) ; BONOMI; ALBERTO GIOVANNI;
(EINDHOVEN, NL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
Eindhoven |
|
NL |
|
|
Family ID: |
1000005277867 |
Appl. No.: |
16/853262 |
Filed: |
April 20, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15564593 |
Oct 5, 2017 |
10624580 |
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PCT/EP2016/057825 |
Apr 8, 2016 |
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16853262 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 2562/0219 20130101;
A61B 5/4866 20130101; A61B 5/02416 20130101; A61B 5/1118 20130101;
A61B 5/681 20130101; A61B 5/0205 20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/024 20060101 A61B005/024; A61B 5/11 20060101
A61B005/11; A61B 5/0205 20060101 A61B005/0205 |
Foreign Application Data
Date |
Code |
Application Number |
Apr 9, 2015 |
EP |
15162997.9 |
Claims
1. A method for measuring an overall activity expenditure of a user
with a vital signs monitoring device, comprising the steps of:
receiving heart rate data from at least one heart rate sensor of
the vital signs monitoring device, the sensor configured to measure
or determine a heart rate of the user; receiving motion or
acceleration data from at least one motion sensor of the vital
signs monitoring device, the sensor configured to detect motion or
acceleration data of the user; determining, by a processor of the
vital signs monitoring device, a first activity energy expenditure
based on the received heart rate data; determining, by the
processor, a second activity energy expenditure based on the
received motion or acceleration data; calculating, by the
processor, a first probability of a high exertion of the user and a
second probability of a low exertion of the user; extracting, by
the processor, a feature set F based on at least one of the heart
rate data and the motion or acceleration data, wherein the feature
set F serve as predictors of a high or low exertion; classifying,
by the processor, an exertion level and outputting the first and
second probability as a function of the feature set F; determining,
by the processor, a first weighting factor based on the first
probability and a second weighting factor based on the second
probability; and computing, by the processor, an overall activity
expenditure based on: (i) the first activity energy expenditure
based on heart rate data and weighted by the first weighting
factor; and (ii) the second activity energy expenditure based on
motion or acceleration data and weighted by the second weighting
factor; wherein the first probability of a high exertion of the
user (h.sub.H) is calculated using the formula: h.sub.H(F)=P(y=1/F;
B.sub.c), and the second probability of a low exertion of the user
(h.sub.L) is calculated using the formula: h.sub.L(F)=P(y=0|F;
B.sub.c), where B.sub.c is a parameter set derived during a
training phase for the user.
2. The method of claim 1, wherein the at least one heart rate
sensor is a photoplethysmographic sensor.
3. The method of claim 1, wherein the at least one heart rate
sensor comprises a contact surface configured to contact skin of
the user.
4. The method of claim 1, wherein the at least one motion sensor is
an accelerometer.
5. The method of claim 1, wherein the first weighting factor and
the second weighting factor are a value between and including 0 and
1.
6. The method of claim 1, wherein the step of classifying an
exertion level by the processor comprises parameter pc configured
to control a sensitivity of the exertion level classification.
7. The method of claim 1, wherein the step of determining a first
activity energy expenditure based on the received heart rate data
comprises parameter P.sub.HR configured to control a sensitivity of
the determining the first activity energy expenditure.
8. The method of claim 1, wherein the step of determining a second
activity energy expenditure based on the received motion or
acceleration data comprises parameter P.sub.AC configured to
control a sensitivity of the determining the second activity energy
expenditure.
9. The method of claim 1, wherein the vital signs monitoring device
is a wearable device.
10. The method of claim 9, wherein the wearable vital signs
monitoring device is configured to be worn the user's arm, wrist,
or hand.
11. The method of claim 9, wherein the wearable vital signs
monitoring device is configured to be worn on or about the user's
head.
12. A wearable system configured to monitor a user, comprising: at
least one heart rate sensor configured to measure or determine a
heart rate of the user; at least one motion sensor configured to
detect motion or acceleration data of the user; and a processor
configured to: (i) determine a first activity energy expenditure
based on the received heart rate data; (ii) determine a second
activity energy expenditure based on the received motion or
acceleration data; (iii) calculate a first probability of a high
exertion of the user and a second probability of a low exertion of
the user; (iv) extract a feature set F based on at least one of the
heart rate data and the motion or acceleration data, wherein the
feature set F serve as predictors of a high or low exertion; (v)
classify an exertion level and outputting the first and second
probability as a function of the feature set F; (vi) determine a
first weighting factor based on the first probability and a second
weighting factor based on the second probability; and (vii) compute
an overall activity expenditure based on the first activity energy
expenditure based on heart rate data and weighted by the first
weighting factor, and the second activity energy expenditure based
on motion or acceleration data and weighted by the second weighting
factor; wherein the first probability of a high exertion of the
user (h.sub.H) is calculated by the processor using the formula:
h.sub.H(F)=P(y=1/F; B.sub.c), and the second probability of a low
exertion of the user (h.sub.L) is calculated using the formula:
h.sub.L(F)=P(y=0|F; B.sub.c), where B.sub.c is a parameter set
derived during a training phase for the user.
13. The wearable system of claim 12, wherein the at least one heart
rate sensor is a photoplethysmographic sensor.
14. The wearable system of claim 12, wherein the at least one heart
rate sensor comprises a contact surface configured to contact skin
of the user.
15. The wearable system of claim 12, wherein the vital signs
monitoring device is a wearable device.
16. The wearable system of claim 12, wherein the first weighting
factor and the second weighting factor are a value between and
including 0 and 1.
17. The wearable system of claim 12, wherein classifying an
exertion level by the processor comprises parameter pc configured
to control a sensitivity of the exertion level classification.
18. The wearable system of claim 12, wherein determining a first
activity energy expenditure by the processor based on the received
heart rate data comprises parameter P.sub.HR configured to control
a sensitivity of the determining the first activity energy
expenditure.
19. The wearable system of claim 12, wherein determining a second
activity energy expenditure by the processor based on the received
motion or acceleration data comprises parameter P.sub.AC configured
to control a sensitivity of the determining the second activity
energy expenditure.
20. The wearable system of claim 12, wherein the wearable system is
a wearable device configured to be worn the user's arm, wrist, or
hand, or to be worn on or about the user's head.
Description
CROSS-REFERENCE TO PRIOR APPLICATIONS
[0001] This application claims the benefit of, and is a
continuation of, co-pending U.S. application Ser. No. 15/564,593
filed Oct. 5, 2017 which is a national application of PCT
Application No. PCT/EP2016/057825 filed Apr. 8, 2016 and claims the
benefit of European Application No. 15162997.9 filed Apr. 9, 2015
and is hereby incorporated by reference in its entirety herein.
FIELD OF THE INVENTION
[0002] The invention relates to a vital signs monitoring system as
well as to a method of monitoring vital signs or physiological
parameters of a user.
BACKGROUND OF THE INVENTION
[0003] Heart rate sensors are well known to monitor or detect vital
signs like a heart rate of a user. Such a heart rate sensor can be
an optical heart rate sensor based on a photo-plethysmographic
(PPG) sensor and can be used to acquire a volumetric organ
measurement. By means of PPG sensors, changes in light absorption
of a human skin is detected and based on these measurements a heart
rate or other vital signs of a user can be determined. The PPG
sensors comprise a light source like a light emitting diode (LED)
which is emitting light into the skin of a user. The emitted light
is scattered in the skin and is at least partially absorbed by the
blood. Part of the light exits the skin and can be captured by a
photodiode. The amount of light that is captured by the photo diode
can be an indication of the blood volume inside the skin of a user.
A PPG sensor can monitor the perfusion of blood in the dermis and
subcutaneous tissue of the skin through an absorption measurement
at a specific wavelength. If the blood volume is changed due to the
pulsating heart, the scattered light coming back from the skin of
the user is also changing. Therefore, by monitoring the detected
light signal by means of the photodiode, a pulse of a user in his
skin and thus the heart rate can be determined. Furthermore,
compounds of the blood like oxygenated or de-oxygenated hemoglobin
as well as oxygen saturation can be determined.
[0004] FIG. 1 shows a basic representation of an operational
principle of a heart rate sensor. In FIG. 1, a heart rate sensor
100 is arranged on an arm of a user. The heart rate sensor 100
comprises a light source 110 and a photo detector 120. The light
source 110 emits light onto or in the skin 1000 of a user. Some of
the light is reflected and the reflected light can be detected by
the photo detector 120. Some light can be transmitted through
tissue of the user and be detected by the photo detector 120. Based
on the reflected or transmitted light, vital signs of a user like a
heart rate can be determined.
[0005] The results of a heart rate sensor can be used to estimate
or measure a caloric Activity Energy Expenditure AEE.
[0006] FIG. 2 shows a representation of a total energy expenditure
of a human being. The Total Energy Expenditure TEE is composed of a
Basal Energy Expenditure BEE, a diet induced thermogenesis DIT and
an Activity Energy Expenditure AEE. The Basal Energy Expenditure
BEE is a combination of the sleeping metabolic rate and the energy
expenditure from arousal.
[0007] If a user wants to for example reduce his weight, he must
burn more calories than he is eating or drinking. The Activity
Energy Expenditure AEE is that part of the Total Energy Expenditure
TEE which is influenced by the activity of the person.
[0008] When a user is trying to reduce weight, it is often not easy
for the user to determine how many calories he has spent throughout
an activity or workout. Hence, there is a need for an accurate
estimation or measurement of the energy spent during an
activity.
[0009] An accurate estimation or measurement of the caloric
Activity Energy Expenditure AEE is therefore an important factor
for example for smart watches enabling sport and wellbeing
applications.
[0010] Accordingly, it is desired to provide a monitor which can
monitor the activity of a user during the day and which can measure
or estimate the Activity Energy Expenditure, i.e. the energy
expenditure of a user during a day.
[0011] FIG. 3 shows a graph indicating an Activity Energy
Expenditure AEE prediction as a function of a measured Activity
Energy Expenditure. In FIG. 3, the measured Activity Energy
Expenditure MAEE is depicted at the X-axis and the predicted
Activity Energy Expenditure PAEE is depicted at the Y-axis.
Furthermore, in FIG. 3, several activity types like running,
cycling, rowing, using a cross trainer etc. are depicted as data
points. For some activities like walking, an overestimation OE (the
estimated Activity Energy Expenditure AEE is too high) can be
present. For other activities like cycling, rowing and using a
cross trainer, an underestimation UE (the estimated Activity Energy
Expenditure AEE is too low) can be present. In FIG. 3, furthermore,
the optimal estimation OES is also depicted, namely the situation
where the measured Activity Energy Expenditure MAEE corresponds to
the predicted Activity Energy Expenditure.
[0012] The reasons why the measured and predicted Activity Energy
Expenditure do not correspond to each other can be that the model
based on which the predicted Activity Energy Expenditure AEE is
determined is not accurate enough or the activity which the user is
performing is not reflected good enough in the model.
[0013] According to FIG. 3, some activities requiring a high
physical activity level may be misinterpreted or underestimated
like cycling, rowing and using a cross trainer. In addition or
alternatively to using heart rate data, the Activity Energy
Expenditure AEE can be determined or estimated for example based on
motion data of a user acquired from an acceleration sensor.
[0014] If a heart rate of a user is used to estimate the Activity
Energy Expenditure AEE, it should be noted that the known linear
relationship between the heart rate and the energy expenditure is
only valid for aerobic activities with a moderate or vigorous
exertion level. Furthermore, heart rate data which is measured for
example during mental stress and fatigue may cause a biased
prediction output in particular for low intensity activities.
Furthermore, motion artifacts may be present in heart rate data.
These motion artifacts may in particular occur during activities,
which show an unpredictable thus non-repetitive movement pattern.
Examples of such movements are several normal daily activities when
full body motion is not represented by hand and wrist movement.
[0015] Furthermore, it should be noted that Activity Energy
Expenditure AEE can be predicted quite accurately based on heart
rate data during aerobic activities while acceleration and movement
information are most suitable to predict Activity Energy
Expenditure during sedentary, low intensity activities with a low
exertion level or non-structured activities.
[0016] WO 2014/207294 A1 discloses a system for monitoring physical
activity based on monitoring motion data of a user or alternatively
based on a heart rate activity of a user.
[0017] EP 1 424 038 A1 discloses a device for measuring a calorie
expenditure.
[0018] US 2008/0139952 A1 discloses a biometric information
processing device which can display a calorie expenditure.
[0019] S. Brage. "Branched equation modeling of simultaneous
accelerometry and heart rate monitoring improves estimate of
directly measured physical activity energy expenditure", Journal of
Applied Physiology, vol. 96, no. 1, 29 Aug. 2003, pages 343-351,
discloses a vital signs monitoring system which computes an overall
activity energy expenditure of a user. Static weight coefficients
are determined offline during a training phase of a system. This is
in particular performed by minimizing the root-mean-square error
between a reference physical activity energy expenditure and an
estimated activity energy expenditure derived from a mode with
weight coefficients.
SUMMARY OF THE INVENTION
[0020] It is an object of the invention to provide a vital signs
monitoring system which is able to accurately predict or measure an
Activity Energy Expenditure of a user.
[0021] According to an aspect of the invention, a vital signs
monitoring system is provided which comprises a processing unit
configured to estimate an activity energy expenditure of a user.
The processing unit comprises a first input configured to receive
heart rate data HR from at least one heart rate sensor configured
to measure or determine a heart rate HR of a user and a second
input configured to receive motion or acceleration data AC from at
least one motion sensor configured to detect motion or acceleration
data AC of a user. The processing unit further comprises a first
activity energy expenditure determining unit configured to
determine a first activity energy expenditure based on heart rate
data HR received via the first input. The processing unit further
comprises a second activity energy expenditure determining unit
configured to determine a second activity energy expenditure based
on motion or acceleration data received via the second input. The
processing unit furthermore comprises an estimation unit configured
to estimate an exertion level of a user based on the current heart
rate data from the at least one heart rate sensor and/or the
current motion or acceleration data from the at least one motion
sensor by estimating a first probability of a high exertion of a
user and a second probability of a low exertion of a user. The
processing unit further comprises a weighting unit configured to
determine a first and second weighting factor based on a first
probability relating to a high exertion level of the user and a
second probability relating to a low exertion level of the user.
The processing unit further comprises an activity energy
expenditure calculating unit configured to compute overall activity
energy expenditure based on the first activity energy expenditure
weighted by the first weighting factor and on the second activity
energy expenditure weighted by the second weighting factor.
[0022] According to an aspect of the invention the processing unit
furthermore comprises a probability estimating unit configured for
estimating a probability of a high or low exertion of a user. The
weighting unit is then configured to determine the first and second
weighting factor based on the probabilities of a high or low
exertion level.
[0023] According to a further aspect of the invention an optical
heart rate sensor in form of a photoplethysmographic sensor is
configured to measure or determine a heart rate of a user.
Moreover, at least one motion or acceleration sensor is configured
to determine motion or acceleration data of a user.
[0024] According to a further aspect of the invention the
processing unit, the at least one heart rate sensor and the at
least one motion or acceleration sensor is arranged in a wearable
device or a smart watch.
[0025] According to a further aspect of the invention a method of
monitoring vital signs or physiological parameters of a user is
provided. Heart rate data HR from at least one heart rate sensor
configured to measure or determine a heart rate HR of a user is
received. Motion or acceleration data from at least one motion
sensor configured to detect motion or acceleration data of a user
is received. A first activity energy expenditure based on heart
rate data, and a second activity energy expenditure based on motion
or acceleration data are determined. A first and second weighting
factor based on a first probability relating to a high exertion
level of a user and a second probability relating to a low exertion
level of a user are determined. Overall activity energy expenditure
is computed based on the first activity energy expenditure weight
by the first weighting factor and on the second activity energy
expenditure weighted by the second weighting factor.
[0026] It shall be understood that a preferred embodiment of the
present invention can also be a combination of the dependent
claims, above embodiments or aspects with respective independent
claims.
[0027] These and other aspects of the invention will be apparent
from and elucidated with reference to the embodiment(s) described
hereinafter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0028] In the following drawings:
[0029] FIG. 1 shows a basic representation of an operational
principle of a vital signs monitoring system,
[0030] FIG. 2 shows a basic representation of a Total Energy
Expenditure of a user,
[0031] FIG. 3 shows a graph depicting an Activity Energy
Expenditure prediction as a function of measured Activity Energy
Expenditure,
[0032] FIG. 4 shows a block diagram of a vital signs monitoring
system according to an aspect of the invention,
[0033] FIG. 5 shows a further block diagram of a vital signs
monitoring system according to an aspect of the invention,
[0034] FIG. 6 shows a block diagram of a vital signs monitoring
system according to a further aspect of the invention, and
[0035] FIG. 7 shows a graph indicating an improved accuracy of a
predicting Activity Energy Expenditure with a vital signs
monitoring system according to an aspect of the invention.
DETAILED DESCRIPTION OF EMBODIMENTS
[0036] FIG. 4 shows a block diagram of a vital signs monitoring
system according to an aspect of the invention. The vital signs
monitoring system is used to monitor vital signs or physiological
parameters of a user. The vital signs monitor system 10 comprises
at least one heart rate sensor 100, at least one motion sensor 200
for measuring or determining motion data or acceleration data AC of
a user, a processing unit 300 such as a processor and optionally a
display 400 with a graphic user interface 410. The processing unit
300 receives heart rate data HR from the at least one heart rate
sensor 100 at its first input 301 and a motion or acceleration data
AC from the at least one motion sensor 200 at its second input 302.
The processing unit 300 is adapted to determine or estimate an
Activity Energy Expenditure AEEO of a user based on the received
heart rate data HR and/or the acceleration data AC. The heart rate
sensor 100 can comprise a contact surface 101 which can be placed
or arranged on a skin 1000 of a user to detect a heart rate of the
user.
[0037] According to an aspect of the invention, an optical vital
signs sensor is provided as a heart rate sensor 100 which is based
on a photoplethysmographic PPG sensor. Such a PPG sensor is
depicted in FIG. 1. A light source 110 emits light onto or into the
skin 1000 of a user and some of the light is reflected and this
reflected light can be detected by a photo detector 120. The output
of the photo detector 120 can be analyzed to determine a heart rate
or other vital signs of a user.
[0038] The output signal of the PPG sensor gives an indication on
the blood movement in vessels of a user. The quality of the output
signal of the PPG sensor can depend on the blood flow rate, skin
morphology and skin temperature. In addition, optical losses in the
PPG sensor may also have an influence on the quality of the output
signal of the PPG sensor. The optical efficiency of the PPG sensor
can depend on reflection losses when light penetrates from one
media into another. Furthermore, a scattering of light at the
surface of the skin of the user may also have an influence on the
optical efficiency of the PPG sensor.
[0039] The PPG sensor or optical vital signs sensor according to an
aspect of the invention can be implemented as a wearable device
e.g. a wrist device (like a watch or smart watch). The optical
vital signs sensor can also be implemented as a device worn behind
the ear of a user, e.g. like a hearing aid. A wearable device is a
device which can be worn or attached on a skin of a user.
[0040] The motion sensor 200 can also be implemented as a wearable
device. Preferably, the motion sensor 120 and the heart rate sensor
110 are implemented in a common housing as part of a wearable
device. The motion sensor 200 can be an acceleration sensor such as
a piezoelectric, piezoresistive or a capacitive accelerometer.
[0041] FIG. 5 shows a further block diagram of a vital signs
monitoring system according to an aspect of the invention. The
vital signs monitor system 10 comprises at least one heart rate
sensor 100, at least one motion or acceleration sensor 200 and a
processing unit 300. The processing unit 300 receives the heart
rate data HR from the at least one heart rate sensor 100 at its
first input 301 as well as a acceleration or motion data AC from
the acceleration sensor 200 at its second input 302. The processing
unit 300 outputs a predicted or estimated Activity Energy
Expenditure AEEO.
[0042] The processing unit 300 can use the heart rate data HR from
the at least one heart rate sensor 100 or the acceleration data AC
from the acceleration sensor 200 to predict or determine an
Activity Energy Expenditure AEE. In other words, the processing
unit 300 can either use the heart rate data HR or the acceleration
data AC. Alternatively, the processing unit 300 may also use a
combination of the heart rate data HR and the acceleration data
AC.
[0043] The processing unit 300 comprises an estimating unit 310 for
estimating the exertion level of a person during a time interval.
The estimating unit 310 receives the current heart rate data HR and
the current acceleration data AC and analyzes these data. The
estimation unit 310 outputs a signal s indicating whether the
exertion level is high or low. The output signal S will correspond
to "1" if the exertion level is estimated as high and will
correspond to "0" if the exertion level is estimated as low. The
estimating unit 310 has a further input for the parameter p.sub.c.
This parameter p.sub.c can be used for controlling the sensitivity
and specificity of the exertion level estimation performed by the
estimation unit 310.
[0044] The processing unit 300 furthermore comprises a first
Activity Energy Expenditure determining unit 320 which computes or
estimates the Activity Energy Expenditure AEE based on the heart
rate HR data from the at least one heart rate sensor 100. The first
Activity Energy Expenditure determining unit 320 comprises a
further input for a parameter set P.sub.HR which can be used to
determine the sensitivity of the first determining unit 320. The
first determining unit 320 outputs an Activity Energy Expenditure
AEEHR for the activity expenditure based on the heart rate data
HR.
[0045] The processing unit 300 furthermore comprises a second
Activity Energy Expenditure determining unit 330 which computes or
determines the Activity Energy Expenditure AEE based on the
acceleration data AC from the at least one acceleration sensor or
motion sensor 200. The second determining unit 330 comprises an
input for a parameter set P.sub.AC for setting the sensitivity of
the second determining unit 330. The second Activity Energy
Expenditure determining unit 330 outputs an Activity Energy
Expenditure AEEAC as determined based on the acceleration or motion
data AC.
[0046] The processing unit 300 furthermore comprises a selecting
unit 340 which selects the output of the first or second Activity
Energy Expenditure determining unit 320, 330 based on the output
signal s of the estimating unit 310. In particular, if the output
signal s is high "1", then the output of the first Activity Energy
Expenditure unit 320 is used while if the output signal is low "0",
then the output of the second Activity Energy Expenditure
determining unit 330 is used as output signal.
[0047] The vital signs monitoring system according to FIG. 5 has
some drawbacks as a decision whether or not a high or low exertion
level is present can sometimes not be accurately performed. Thus,
in some cases, the decision whether or not the exertion level is
high or low is prone to mistakes.
[0048] In addition, if the activity of a user is at the border
between high and low exertion levels due to motion artifacts, the
processing unit may switch between low and high exertion levels,
thus changing the output of the processing unit and thereby the
overall Activity Energy Expenditure during the physical activity
such that the user may not receive an accurate and constant
estimation of the activity level.
[0049] FIG. 6 shows a block diagram of a vital signs monitoring
system according to a further aspect of the invention. The vital
signs monitor system 10 comprises at least one heart rate sensor
100, at least one acceleration or motion sensor 200 and a
processing unit 300. The heart rate sensor 100 outputs heart rate
data HR and the acceleration or motion sensor 200 outputs
acceleration or motion data AC. The processing unit 300 comprises a
first Activity Energy Expenditure determining unit 320 for
determining the Activity Energy Expenditure AEEHR based on the
heart rate data HR from the at least one heart rate sensor 100. The
processing unit furthermore comprises a second Activity Energy
Expenditure determining unit 330 for determining the Activity
Energy Expenditure AEEAC based on the motion data or acceleration
data AC from the at least one acceleration sensor 200. The
processing unit 300 furthermore comprises a probability unit 360
for determining a probability of a high or low exertion based on
the heart rate data HR from the at least one heart rate sensor 100
as well as based on current acceleration or motion data AC from the
at least one acceleration sensor 200. In other words, the
probability unit 360 serves to estimate the probability of a high
exertion or a low exertion level. The probability estimating unit
360 comprises a feature set unit 361 for extracting a feature set F
based on the heart rate data HR, the motion data or acceleration
data AC or a combination of both. The feature set F are used as
predictors for a high or low body exertion. These predictors can be
an activity count, speed, number of steps, motion level etc. The
motion level can be used together with cardiac features from the
heart rate data.
[0050] The feature set F can be inputted into a classification unit
362. The classification unit 362 is used to classify the exertion
level of a person. The classification unit 362 can receive a
parameter set Bc. This parameter Bc can optionally be derived
during a training phase. The classification unit 362 outputs a
probability of the high and low exertion as a function of the
feature set F. A High exertion level correspond to aerobic
activities, activities having a consistent and repetitive temporal
pattern, which may represent planned actions of exertion level
higher than resting low exertion level consists of activities
involving irregular body movement, which result in predominant
anaerobic work or low intensity sedentary occupations. The
classification unit 362 may also use an estimated resting heart
rate, an estimated maximum heart rate, the sex, age, height and
weight of the user during the classification process. Optionally,
the probabilities can be defined as h.sub.H (F)=P (y=1|F; B.sub.c)
and h.sub.L (F)=P (y=0|F; B.sub.c).
[0051] The processing unit 300 furthermore comprises a weighting
unit 340 for transforming the probability estimates h.sub.H and
h.sub.L into weighting factors wHR and wAC. The weighting factors
can be between 0 and 1.
[0052] The processing unit 300 furthermore comprises an Activity
Energy Expenditure calculating unit 350, which is computing or
determining the overall Activity Energy Expenditure AEEO. The
overall Activity Energy Expenditure AEEO is defined as: f.sub.WA
(wHR, wAC, AEEHR, AEEAC) such that the overall Activity Energy
Expenditure AEEO=wHRAEEHR+wACAEEAC. Accordingly, the overall
Activity Energy Expenditure is based on a combination of the
Activity Energy Expenditure determined based on the heart rate data
HR as well as on the acceleration data AC with corresponding
weighting factors.
[0053] According to a further aspect of the invention, instead of
using two different parameters like heart rate HR and motion data
AC for determining the Activity Energy Expenditure, the data of a
plurality of sensors can be used to determine the Activity Energy
Expenditure. The Activity Energy Expenditure AEE can be determined
or estimated based on each of the N sensors such that each i.sub.th
estimation unit produces an energy expenditure estimate. As in the
aspect of the invention according to FIG. 6, weighting units are
provided for analyzing the output signals of the sensor and
optionally for incorporating supplementary subject specific
information. The overall Activity Energy Expenditure AEE is then
estimated by computing a weighted average of all separate energy
expenditure estimates.
[0054] FIG. 7 shows a graph indicating an improved accuracy of a
predicting Activity Energy Expenditure AEE with a vital signs
monitoring system according to an aspect of the invention. In FIG.
7, a comparison is depicted between the aspect of the invention
according to FIG. 5 and according to FIG. 6. The results as
achieved by the vital signs monitor system according to FIG. 5 are
depicted as Exp A while the results achieved by the vital signs
monitor system according to FIG. 6 are depicted as Exp B. In
particular, three different tests have been performed, namely a
step test ST, a walking at 3 km/h W and using a cross trainer CT
has been used.
[0055] In table 1, examples of the weighting factors wHR and wAC
are depicted.
TABLE-US-00001 Activity type wHR wAC Step Test ST 0.79 0.21 Walking
3 km/h W 0.97 0.03 Cross Trainer CT 0.83 0.17
[0056] For the walking activity W, the weighting factors of the
mixture of the Activity Energy Expenditure AEEHR based on the heart
rate and the Activity Energy Expenditure AEEAC based on the
acceleration data AC has no limiting effect as the weighting factor
for the heart rate data is approximately 1, i.e. the overall
Activity Energy Expenditure corresponds to the Activity Energy
Expenditure based on the heart rate data. On the other hand, for
the step test T and the cross trainer CT, the weighting factor
w.sub.HR corresponds to 0.79 and 0.83, respectively while the
weighting factor for the acceleration data corresponds to 0.21 and
0.17, respectively. Hence, the overall Activity Energy Expenditure
can be calculated more accurately.
[0057] Table 2 shows the average RMSE as obtained from the two
experiments described before.
TABLE-US-00002 Exp A (kcal/min) Exp B (kcal/min) Step Test ST 1.67
1.47 Walking 3 km/h W 2.58 2.58 Cross Trainer CT 1.59 1.40
[0058] Accordingly, it can be seen that the average RMSE obtained
from the second experiment has improved by 12% with respect to the
first experiments. Regarding the walking exercise W, the RMSE
substantially remains constant. Accordingly, the provision of the
weighting factors is advantageous to estimate an exertion
level.
[0059] In table 3, the standard deviations of the overall Activity
Energy Expenditure AEE for the two experiments are disclosed.
TABLE-US-00003 Exp A (kcal/min) Exp B (kcal/min) Step Test ST 1.05
0.88 Walking 3 km/h W 1.95 1.92 Cross Trainer CT 1.55 1.28
[0060] As can be seen from FIG. 3, by using the vital signs
monitoring system according to FIG. 6, it is possible to reduce the
variability in the overall Activity Energy Expenditure AEE. For the
walking exercise W, the variation of the overall Activity Energy
Expenditure remains substantially unchanged.
[0061] According to an aspect of the invention, a computer program
product is provided which comprises: a computer readable memory
storing computer program code means for causing the vital signs
monitor system to carry out steps of the monitoring vital signs or
physiological parameters of a user.
[0062] Other variations of the disclosed embodiment can be
understood and effected by those skilled in the art in practicing
the claimed invention from a study of the drawings, the disclosure
and the appended claims.
[0063] In the claims, the word "comprising" does not exclude other
elements or steps and in the indefinite article "a" or "an" does
not exclude a plurality.
[0064] A single unit or device may fulfill the functions of several
items recited in the claims. The mere fact that certain measures
are recited in mutual different dependent claims does not indicate
that a combination of these measurements cannot be used to
advantage. A computer program may be stored/distributed on a
suitable medium such as an optical storage medium or a solid state
medium, supplied together with or as a part of other hardware, but
may also be distributed in other forms such as via the internet or
other wired or wireless telecommunication systems.
[0065] Any reference signs in the claims should not be construed as
limiting the scope.
* * * * *