U.S. patent application number 16/049668 was filed with the patent office on 2018-11-22 for smart garments that identify user changes.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Subil M. Abraham, Marco A. Benavides, Diana P. Cabrera, Stephanie De La Fuente.
Application Number | 20180336774 16/049668 |
Document ID | / |
Family ID | 62948609 |
Filed Date | 2018-11-22 |
United States Patent
Application |
20180336774 |
Kind Code |
A1 |
Abraham; Subil M. ; et
al. |
November 22, 2018 |
SMART GARMENTS THAT IDENTIFY USER CHANGES
Abstract
Sensor data generated by a portion of a plurality of sensors
integrated into a smart garment is received, the sensor data
indicating at least one biometric parameter indicating a state of
health of a user wearing the smart garment. Based on the first
sensor data, at least one health change parameter indicating at
least one change in the state of health of the user can be
determined. Based on the at least one health change parameter,
whether the at least one change in the state of health of the user
exceeds a threshold value can be determined. Responsive to
determining that the at least one change in the state of health of
the user exceeds the threshold value, a notification indicating
that the at least one change in the state of health of the user
exceeds the threshold value can be output.
Inventors: |
Abraham; Subil M.;
(Lewisville, TX) ; Benavides; Marco A.; (Aubrey,
TX) ; Cabrera; Diana P.; (Flower Mound, TX) ;
De La Fuente; Stephanie; (Aubrey, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
62948609 |
Appl. No.: |
16/049668 |
Filed: |
July 30, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
15473263 |
Mar 29, 2017 |
10037672 |
|
|
16049668 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/024 20130101;
A61B 5/0816 20130101; G08B 21/043 20130101; G08B 21/0453 20130101;
A61B 5/0008 20130101; A61B 5/6804 20130101; A61B 5/02055 20130101;
A61B 5/4266 20130101; A61B 5/021 20130101 |
International
Class: |
G08B 21/04 20060101
G08B021/04; A61B 5/0205 20060101 A61B005/0205; A61B 5/00 20060101
A61B005/00 |
Claims
1-20. (canceled)
21. A method, comprising: receiving first sensor data generated by
at least a first portion of a plurality of sensors integrated into
a smart garment, the first sensor data indicating at least one
biometric parameter indicating a state of health of a user wearing
the smart garment; determining, using a processor, based on the
first sensor data, at least one health change parameter indicating
at least one change in the state of health of the user; based on
the at least one health change parameter indicating the at least
one change in the state of health of the user, determining whether
the at least one change in the state of health of the user exceeds
a threshold value; and responsive to determining that the at least
one change in the state of health of the user exceeds the threshold
value, outputting a first notification indicating that the at least
one change in the state of health of the user exceeds the threshold
value.
22. The method of claim 21, further comprising: outputting a second
notification indicating a recommendation for the user to take at
least one action to mitigate a health risk resulting from the
change in the state of health of the user.
23. The method of claim 22, further comprising: analyzing the at
least one biometric parameter; and based on the analyzing the at
least one biometric parameter, determining at least one pattern in
the at least one biometric parameter; wherein outputting the second
notification indicating the recommendation for the user to take the
at least one action to mitigate the health risk resulting from the
change in the state of health of the user comprises determining the
recommendation based on the determined at least one pattern in the
at least one biometric parameter.
24. The method of claim 21, further comprising: determining, based
on the first sensor data, a risk of disease for the user;
determining whether the risk of the disease for the user exceeds a
threshold value; and responsive to determining that the risk of the
disease for the user exceeds the threshold value, outputting a
second notification indicating the risk of the disease for the
user.
25. The method of claim 24, further comprising: analyzing the at
least one biometric parameter; and based on the analyzing the at
least one biometric parameter, determining at least one pattern in
the at least one biometric parameter; wherein outputting the second
notification indicating the risk of the disease for the user
comprises determining the risk of the disease for the user based on
the determined at least one pattern in the at least one biometric
parameter.
26. The method of claim 25, further comprising: receiving second
sensor data generated by at least a second portion of the plurality
of sensors integrated into the smart garment, the second sensor
data indicating at least one size change parameter that indicates a
weight gain of the user or a weight loss of the user; wherein the
determining the risk of the disease for the user further is based
on the second sensor data indicating the at least one size change
parameter.
27. The method of claim 21, wherein the biometric parameter is a
parameter selected from a group consisting of a body temperature
parameter, a heart rate parameter, and a blood pressure
parameter.
28. The method of claim 21, wherein the biometric parameter is a
parameter selected from a group consisting of a level of
perspiration rate parameter and a blood sugar level parameter.
29. A system, comprising: a processor programmed to initiate
executable operations comprising: receiving first sensor data
generated by at least a first portion of a plurality of sensors
integrated into a smart garment, the first sensor data indicating
at least one biometric parameter indicating a state of health of a
user wearing the smart garment; determining, based on the first
sensor data, at least one health change parameter indicating at
least one change in the state of health of the user; based on the
at least one health change parameter indicating the at least one
change in the state of health of the user, determining whether the
at least one change in the state of health of the user exceeds a
threshold value; and responsive to determining that the at least
one change in the state of health of the user exceeds the threshold
value, outputting a first notification indicating that the at least
one change in the state of health of the user exceeds the threshold
value.
30. The system of claim 29, the executable operations further
comprising: outputting a second notification indicating a
recommendation for the user to take at least one action to mitigate
a health risk resulting from the change in the state of health of
the user.
31. The system of claim 30, the executable operations further
comprising: analyzing the at least one biometric parameter; and
based on the analyzing the at least one biometric parameter,
determining at least one pattern in the at least one biometric
parameter; wherein outputting the second notification indicating
the recommendation for the user to take the at least one action to
mitigate the health risk resulting from the change in the state of
health of the user comprises determining the recommendation based
on the determined at least one pattern in the at least one
biometric parameter.
32. The system of claim 29, the executable operations further
comprising: determining, based on the first sensor data, a risk of
disease for the user; determining whether the risk of the disease
for the user exceeds a threshold value; and responsive to
determining that the risk of the disease for the user exceeds the
threshold value, outputting a second notification indicating the
risk of the disease for the user.
33. The system of claim 32, the executable operations further
comprising: analyzing the at least one biometric parameter; and
based on the analyzing the at least one biometric parameter,
determining at least one pattern in the at least one biometric
parameter; wherein outputting the second notification indicating
the risk of the disease for the user comprises determining the risk
of the disease for the user based on the determined at least one
pattern in the at least one biometric parameter.
34. The system of claim 33, the executable operations further
comprising: receiving second sensor data generated by at least a
second portion of the plurality of sensors integrated into the
smart garment, the second sensor data indicating at least one size
change parameter that indicates a weight gain of the user or a
weight loss of the user; wherein the determining the risk of the
disease for the user further is based on the second sensor data
indicating the at least one size change parameter.
35. The system of claim 29, wherein the biometric parameter is a
parameter selected from a group consisting of a body temperature
parameter, a heart rate parameter, and a blood pressure
parameter.
36. The system of claim 29, wherein the biometric parameter is a
parameter selected from a group consisting of a level of
perspiration rate parameter and a blood sugar level parameter.
37. A computer program product comprising a computer readable
storage medium having program code stored thereon, the program code
executable by a processor to perform a method comprising: receiving
first sensor data generated by at least a first portion of a
plurality of sensors integrated into a smart garment, the first
sensor data indicating at least one biometric parameter indicating
a state of health a user wearing the smart garment; determining, by
the processor, based on the first sensor data, at least one health
change parameter indicating at least one change in the state of
health of the user; based on the at least one health change
parameter indicating the at least one change in the state of health
of the user, determining whether the at least one change in the
state of health of the user exceeds a threshold value; and
responsive to determining that the at least one change in the state
of health of the user exceeds the threshold value, outputting a
first notification indicating that the at least one change in the
state of health of the user exceeds the threshold value.
38. The computer program product of claim 37, the method further
comprising: outputting a second notification indicating a
recommendation for the user to take at least one action to mitigate
a health risk resulting from the change in the state of health of
the user.
39. The computer program product of claim 38, the method further
comprising: analyzing the at least one biometric parameter; and
based on the analyzing the at least one biometric parameter,
determining at least one pattern in the at least one biometric
parameter; wherein outputting the second notification indicating
the recommendation for the user to take the at least one action to
mitigate the health risk resulting from the change in the state of
health of the user comprises determining the recommendation based
on the determined at least one pattern in the at least one
biometric parameter.
40. The computer program product of claim 37, the method further
comprising: determining, based on the first sensor data, a risk of
disease for the user; determining whether the risk of the disease
for the user exceeds a threshold value; and responsive to
determining that the risk of the disease for the user exceeds the
threshold value, outputting a second notification indicating the
risk of the disease for the user.
Description
BACKGROUND
[0001] The present invention relates to smart fabrics and, more
particularly, the use of smart fabrics in smart garments.
[0002] Smart fabrics are fabrics that include electronic
components. Smart fabrics can perform tasks that traditional
fabrics do not. For example, from an aesthetic perspective, smart
fabrics can be illuminated and/or change color. Smart fabrics also
have been developed for protective clothing to guard against
extreme environmental hazards like radiation and the effects of
space travel. The health and beauty industry also is taking
advantage of innovations such as drug-releasing medical fabrics,
and fabrics that include moisturizer, perfume, and anti-aging
properties.
SUMMARY
[0003] A method includes receiving first sensor data generated by
at least a first portion of a plurality of sensors integrated into
a smart garment, the first sensor data indicating at least one
biometric parameter indicating a state of health of a user wearing
the smart garment. The method also can include determining, using a
processor, based on the first sensor data, at least one health
change parameter indicating at least one change in the state of
health of the user. The method also can include, based on the at
least one health change parameter indicating the at least one
change in the state of health of the user, determining whether the
at least one change in the state of health of the user exceeds a
threshold value. The method also can include, responsive to
determining that the at least one change in the state of health of
the user exceeds the threshold value, outputting a first
notification indicating that the at least one change in the state
of health of the user exceeds the threshold value.
[0004] A system includes a processor programmed to initiate
executable operations. The executable operations receiving first
sensor data generated by at least a first portion of a plurality of
sensors integrated into a smart garment, the first sensor data
indicating at least one biometric parameter indicating a state of
health of a user wearing the smart garment. The executable
operations also can include determining based on the first sensor
data, at least one health change parameter indicating at least one
change in the state of health of the user. The executable
operations also can include, based on the at least one health
change parameter indicating the at least one change in the state of
health of the user, determining whether the at least one change in
the state of health of the user exceeds a threshold value. The
executable operations also can include, responsive to determining
that the at least one change in the state of health of the user
exceeds the threshold value, outputting a first notification
indicating that the at least one change in the state of health of
the user exceeds the threshold value.
[0005] A computer program includes a computer readable storage
medium having program code stored thereon. The program code is
executable by a processor to perform a method. The method includes
receiving first sensor data generated by at least a first portion
of a plurality of sensors integrated into a smart garment, the
first sensor data indicating at least one biometric parameter
indicating a state of health of a user wearing the smart garment.
The method also can include determining, by the processor, based on
the first sensor data, at least one health change parameter
indicating at least one change in the state of health of the user.
The method also can include, based on the at least one health
change parameter indicating the at least one change in the state of
health of the user, determining whether the at least one change in
the state of health of the user exceeds a threshold value. The
method also can include, responsive to determining that the at
least one change in the state of health of the user exceeds the
threshold value, outputting a first notification indicating that
the at least one change in the state of health of the user exceeds
the threshold value.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 is a pictorial diagram illustrating an example of a
smart fabric.
[0007] FIG. 2 is a pictorial diagram illustrating an example of a
smart garment.
[0008] FIG. 3 is a block diagram illustrating an example of a data
processing environment.
[0009] FIG. 4 is a block diagram illustrating example architecture
for a server.
[0010] FIG. 5 is a flow chart illustrating an example of a method
of outputting a notification regarding a size of a smart
garment.
[0011] FIG. 6 is a flow chart illustrating an example of a method
of outputting a notification regarding a risk of disease for a
user.
[0012] FIG. 7 is a flow chart illustrating an example of a method
of outputting a notification regarding a change in the state of
health of a user.
DETAILED DESCRIPTION
[0013] This disclosure relates to smart fabrics and, more
particularly, the use of smart fabrics in smart garments. In
accordance with the inventive arrangements disclosed herein, a
smart garment can include a plurality of sensors that generate
sensor data. The sensor data can include tension data representing
tension, and thus stretch, in the smart garment. The sensor data
can be analyzed and, based on the analyses, any of a variety of
determinations can be made about a user who is wearing the smart
garment. For example, a determination can be made as to whether a
size of the smart garment is suitable for the user. If not, a
notification can be output to indicate to the user that the size is
not suitable for the user. Further, the notification can indicate
an appropriate size that would be suitable for the user and/or a
particular garment having a size that is suitable for the user.
[0014] The sensor data further can include biometric parameters
representing a state of health of the user. Based on the sensor
data, determinations regarding the user's health can be made. Based
on such determinations, any of a variety of notifications can be
communicated to the user and/or a care giver of the user. By way of
example, the notifications can provide recommendations to seek
medical attention, to rest, to exercise, and so on. In a further
example, based on the sensor data, a determination of whether the
user has a risk, greater than a threshold value, of disease. The
notifications can indicate such risk, and provide recommendations
to mitigate the risk.
[0015] Several definitions that apply throughout this document now
will be presented.
[0016] As defined herein, the term "smart garment" means a garment
made, at least in part, of smart fabric.
[0017] As defined herein, the term "smart fabric" means a fabric
that includes at least at least one electronic component.
[0018] As defined herein, the term "size" means a physical
dimension.
[0019] As defined herein, the term "suitable" means appropriate for
an intended purpose.
[0020] As defined herein, the term "client device" means a
processing system including at least one processor and memory that
requests shared services from a server, and with which a user
directly interacts. Examples of a client device include, but are
not limited to, a workstation, a desktop computer, a mobile
computer, a laptop computer, a netbook computer, a tablet computer,
a smart phone, a personal digital assistant, a smart watch, smart
glasses, a gaming device, a set-top box, a smart television and the
like. Network infrastructure, such as routers, firewalls, switches,
access points and the like, are not client devices as the term
"client device" is defined herein.
[0021] As defined herein, the term "server" means a processing
system including at least one processor and memory that shares
services one or more other systems and/or client devices.
[0022] As defined herein, the term "sensor" means a device that
detects or measures a physical property and outputs corresponding
data.
[0023] As defined herein, the term "processor" means at least one
hardware circuit (e.g., an integrated circuit) configured to carry
out instructions contained in program code. Examples of a processor
include, but are not limited to, a central processing unit (CPU),
an array processor, a vector processor, a digital signal processor
(DSP), a field-programmable gate array (FPGA), a programmable logic
array (PLA), an application specific integrated circuit (ASIC),
programmable logic circuitry, and a controller.
[0024] As defined herein, the term "responsive to" means responding
or reacting readily to an action or event. Thus, if a second action
is performed "responsive to" a first action, there is a causal
relationship between an occurrence of the first action and an
occurrence of the second action, and the term "responsive to"
indicates such causal relationship.
[0025] As defined herein, the term "computer readable storage
medium" means a storage medium that contains or stores program code
for use by or in connection with an instruction execution system,
apparatus, or device. As defined herein, a "computer readable
storage medium" is not a transitory, propagating signal per se.
[0026] As defined herein, the term "output" means storing in memory
elements, writing to display or other peripheral output device,
sending or transmitting to another system, exporting, or similar
operations.
[0027] As defined herein, the term "automatically" means without
user intervention.
[0028] As defined herein, the term "user" means a person (i.e., a
human being).
[0029] FIG. 1 is a pictorial diagram illustrating an example of a
smart fabric 100. The smart fabric 100 can include a processor 105.
The smart fabric 100 also can include an RF transmitter
(hereinafter "transmitter") 110 configured to transmit RF signals.
In one arrangement, the transmitter 110 can be a component of a
transceiver that also includes an RF receiver, although the present
arrangements are not limited in this regard. The smart fabric 100
also can include a plurality of sensors 115 integrated into the
smart fabric 100.
[0030] The processor 105 can include a computer readable storage
medium, for example an erasable programmable read-only memory
(EPROM or Flash memory), in which computer program code is stored.
The computer program code can be executed by the processor, as will
be described. The processor 105 also can include an accelerometer
that detects movement, and/or any other suitable sensors or
measurement components. Further, the processor 105 can include a
plurality of input/output (I/O) ports to connect the processor to
other devices, such as the transmitter 110 and the plurality of
sensors 115. In one arrangement, the transmitter 110 can be a
component of the processor 105.
[0031] Each of the plurality of sensors 115 can be communicatively
linked to the processor 105, and the processor 105 can be
communicatively linked to the transmitter 110. Electrical
conductors (not shown) can be integrated into the smart fabric 100
to provide communication links between the processor 105 and the
transmitter 110 and sensors 115. The smart fabric 100 also can
include an energy source 120 that provides power to the processor
105, transmitter 110 and, optionally, the sensors 115. The energy
source 120 can include, for example, a battery, a solar cell, a
piezo electric charger, an inductive power supply, and/or any other
devices that generate and/or provide electricity. In the case that
the energy source 120 is an inductive power supply, the inductive
power supply can generate electricity in response to a magnetic
field generated by an inductive charger, as is known to those of
ordinary skill in the art. Power can be conveyed from the energy
source 120 to the processor 105, transmitter 110 and, optionally,
the sensors 115 via electrical conductors.
[0032] In one arrangement, the electrical conductors can be
embedded in threads of the smart fabric 100, for example by
spinning the electrical conductors into the threads. In another
arrangement, the electrical conductors can be woven with the
threads into the smart fabric 100. Further, the sensors 115 can be
embedded into the threads of the smart fabric 100 when the threads
are spun or can be embedded into the smart fabric 100 when the
smart fabric 100 is woven from the threads. The processor 105 and
transmitter 110 also can be embedded into the smart fabric 100 when
the smart fabric 100 is woven from the threads, or can be attached
to the smart fabric 100 after the smart fabric 100 is woven.
[0033] In another arrangement, the processor 105, transmitter 110
and sensors 115 can be embedded in a flexible material that is
configured to be attached to fabric to form the smart fabric 100.
For example, the flexible material can include a substrate into
which the processor 105, transmitter 110, sensors 115 and
conductors are embedded. The flexible material can include an
adhesive on at least one side configured to attach the flexible
material to the fabric. In illustration, the adhesive can be
configured to be activated with heat and/or light to bond the
flexible material to the fabric. In an arrangement in which the
adhesive is heat activated, the processor 105, transmitter 110,
sensors 115 and conductors can be configured to withstand the
amount of activation heat without becoming damaged during the
process of attaching the flexible material to the fabric.
[0034] The transmitter 110 can be configured to receive signals
from the processor 105, encode the signals, modulate the signals,
etc. to generate corresponding RF signals. For example, the
transmitter 110 can generate RF signals in accordance with a
suitable RF communication protocol, for example in accordance with
one more IEEE 802-15 standards (e.g., Bluetooth.RTM.,
Bluetooth.RTM. low energy (BLE), Zigbee.RTM., and so on) and/or
near field communication (NFC).
[0035] In one aspect, at least a portion of the sensors 115 can be
tension sensors (e.g., piezoelectric tension sensors, which are
known in the art) configured to output to the processor 105
respective signals corresponding to an amount of tension, and thus
stretch, of the smart fabric 100. In a further arrangement, at
least a portion of the sensors 115 can be thermal sensors
configured to output to the processor 105 respective signals
corresponding to a temperature of a user wearing the smart fabric
100. Also, at least a portion of the sensors 115 can be
perspiration sensors (or moisture sensors) configured to output to
the processor 105 respective signals corresponding to a level of
perspiration (or moisture) of a user wearing the smart fabric 100.
Further, at least a portion of the sensors 115 can be respiration
sensors configured to output to the processor 105 respective
signals corresponding to a level of respiration of a user wearing
the smart fabric 100. In a further arrangement, at least a portion
of the sensors 115 can be heart rate sensors configured to detect a
heart rate of a user wearing the smart fabric 100.
[0036] Also, at least a portion of the sensors 115 can be blood
pressure sensors configured to output to the processor 105
respective signals corresponding to a level of blood pressure of a
user wearing the smart fabric 100. In illustration, a garment into
which the smart fabric 100 is incorporated can include a blood
pressure cuff. The processor 105 can actuate the blood pressure
cuff to expand with a compressed gas (e.g., air), and then slowly
decompress. A portion of the sensors 115 can be configured to
detect a systolic value and a diastolic value of the user's
pulse.
[0037] In addition, at least a portion of the sensors 115 can be
can be capacitive sensors configured to output to the processor 105
respective signals indicating whether the smart fabric 100 is being
worn. For example, such sensors can be configured to detect and
indicate a proximity of the sensors 115 to biological tissue. For
example, when a garment made of the smart fabric 100 is worn, one
or more sensors 115 may be placed proximate to a user's skin (e.g.,
within 0.5 mm, 1 mm, 2 mm, 3 mm, 4 mm, 5 mm, etc.), and the signals
can indicate such.
[0038] Still, other types of sensors 115 can be utilized, and the
present arrangements are not limited in this regard. In one
arrangement, more than one type of sensor 115 can be used. For
example, the plurality of sensors 115 can include one or more of
the previously described sensors 115 and/or one or more other types
of sensors.
[0039] In one non-limiting arrangement, each sensor 115 also can
include a radio frequency identifier (RFID) tag. Each RFID tag can
include a computer readable storage medium, for example an erasable
programmable read-only memory (EPROM or Flash memory), configured
to store respective data for the sensor 115. The data can include a
unique identifier for the respective sensor 115. In addition, each
RFID tag also can include a receiver (or transceiver), a decoder, a
power supply and a processor configured to detect an RF signal,
decode the RF signal to identify data contained in the RF signal,
and also store the data contained in the RF signal in the computer
readable storage medium. The power supply can generate energy for
the decoder and processor to operate from energy contained in the
RF signal, as is known in the art. As will be described, the data
contained in the RF signal can indicate in which component of a
smart garment the sensor 115 is integrated.
[0040] FIG. 2 is a pictorial diagram illustrating an example of a
smart garment 200. The smart garment 200 can include the smart
fabric 100 of FIG. 1. In illustration, the smart garment 200 can be
made of the smart fabric 100. The smart garment 200 can be a shirt,
a sweater, a jacket, pants, a skirt, a dress, a hospital gown, a
shoe, or any other type of garment.
[0041] In one arrangement, different components 205, 210, 215, 220
of the smart garment 200 can be cut from the smart fabric 100, and
perhaps one or more other smart fabrics (not shown) following a
garment pattern, and the components 205-220 can be sewn together to
create the smart garment 200. The processor 105, transmitter 110
and energy source 120 can be integrated into a respective portion
of the smart fabric 100 used for any of the smart garment
components 205-220, and the present arrangements are not limited in
this regard.
[0042] During the cutting process, various electrical conductors
may be cut. During the sewing process, electrical connections to
the sensors 115 can be re-established by connecting ends respective
ends of electrical conductors. For example, at a seam 230 where a
sleeve 210 is connected to a front 215 and back 220 of the smart
garment 200, there may be electrical conductors in the sleeve 210,
front 215 and back 220 that have been cut, and the electrical
conductors of the sleeve 210 can be attached to the electrical
conductors of the front 215 and back 220 of the smart garment 200
to form continuous electrical connections between the processor 105
and the sensors 115. Since the sleeve 210, front 215 and back 220
may be cut from different portions of the smart fabric 100, the
electrical path between the processor 105 and each sensor 115 in
the sleeve 210 need not be the same electrical path that was
between the processor 105 and each of such sensors 115 in the smart
fabric 100 prior to the components 205-220 being cut from the smart
fabric 100. The respective ends of the electrical conductors may be
connected at the seam 230 by a person (e.g., a seamstress) while
sewing the smart garment 200 or by a robot configured to perform
such operation. The respective ends of the electrical conductors
may be connected by soldering or welding the respective ends of the
electrical conductors together, or using electrical connectors. The
other components 205-220 of the smart garment 200 can be sewn, and
respective ends of electrical conductors can be connected, in a
similar manner.
[0043] At some point during manufacturing of the smart garment 200,
for example after the components 205-220 have been cut from the
smart fabric 100, each of the components 205-220 can be scanned
using an RF scanner, such as an RFID scanner. The RFID scanner can
be configured to scan each component 205-220 and communicate to the
RFID tags of the respective sensors 115 data indicating in which
component 205-220, and where in the component 205-220, the sensors
115 are integrated. For example, for a lower part of the sleeve
210, a person or automated system can enter data indicating "lower
left sleeve" into the RFID scanner, and scan the portion of the
smart fabric 100 in the lower part of the sleeve 210 with the RFID
scanner. The RFID tag in each sensor 115 of the lower part of the
sleeve 210 can detect the RF signal emitted by the RFID scanner,
and store the data indicating "lower left sleeve" into the
respective computer readable storage medium. The process can be
repeated for each of the components 205-220, as well as different
portions of the components 205-220.
[0044] At some point after the electrical conductors have been
connected, and perhaps after the smart garment 200 is sewn, a
person or automated system can provide to the processor 105
information identifying the smart garment 200, such as a garment
model number, serial number, size, color style, etc. For example,
the person or automated system can scan the processor with an RF
device, such as an RFID scanner, which can communicate to the
processor the data containing the identifying information. The
processor 105 can store the data in the computer readable storage
medium of the processor 105. In this regard, responsive to the
processor 105 receiving, via a receiver (e.g., an RF receiver that
is a component of a transceiver that includes the transmitter 110),
an RF signal containing identifying information, the processor 105
can store data.
[0045] Further, the person or automated system can initiate the
processor 105 to execute the program code of the processor 105 to
retrieve baseline sensor data from the sensors 115 integrated into
various the components 205-220 of the smart garment 200 to generate
baseline measurements for the sensors 115. The processor can
receive energy from the energy source 120, or energy contained in a
received RF signal, to generate the baseline measurements, and can
receive the baseline sensor data via the aforementioned electrical
conductors. A person or automated system can initiate the processor
105 to retrieve the baseline sensor data by depressing a button
integrated into the processor, or scanning the processor with an RF
device. In the case an RF device is used, responsive to receiving
an RF signal containing particular data, the processor 105 can
execute computer program code that causes the processor to poll
each of the sensors 115 integrated into the various the components
205-220 of the smart garment 200.
[0046] The processor 105 can store data received from each sensor
115 in one or more data tables within the computer readable storage
medium of the processor 105. The data retrieved from each sensor
115 can identify the specific sensor 115, indicate in which
component 205-220 respective sensor 115 is integrated, and indicate
a portion of the component 205-220 in which the sensor 115 is
integrated. The data also can include a baseline sensor reading,
for example a tension reading, temperature reading, moisture
reading, etc. detected by the respective sensor 115. For each
respective sensor 115, the processor 105 can create an association
between the sensor identifier, the baseline sensor reading and the
data indicating in which component 205-220, and in which portion of
the components 205-220, the sensor is integrated. As each sensor
115 is polled by the processor 105, the respective sensor 115 can
use energy contained in the polling signal to perform the baseline
sensor reading and communicate the various data to the processor
105. Once the baseline sensor measurements are stored by the
processor 105, the smart garment 200 is ready for packaging and
sale. Of course, tags, etc. can be added to the smart garment 200
if this is desired.
[0047] The processor 105 can be configured to monitor sensor data
generated by the sensors 115, and process the sensor data to
determine if the smart garment 200 is being worn by a user. For
example, responsive to the processor detecting movement (e.g.,
using an accelerometer) or detecting a particular RF signal, the
processor 105 can initiate execution of program code to poll the
sensors 115 to receive sensor data. When a sensor 115 is proximate
to a user's biological tissue (e.g., skin), the sensor 115 can
measure a value of capacitance that is different from a value of
capacitance measured when the sensor 115 is not proximate to the
user's biological tissue (e.g., different from the baseline sensor
measurement). Thus, the processor 105 can be configured to
determine that the sensor 115 is proximate to biological tissue if
the sensor 115 generates a sensor value within a particular range
of sensor values, which can be predetermined.
[0048] Responsive to the processor 105 receiving sensor data from a
threshold number of the sensors 115 indicating that each of those
sensors 115 is proximate to biological tissue, the processor 105
can determine that the smart garment 200 is being worn by a user.
In response, the processor 105 can monitor signals from other
sensors 115 that indicate other parameters, such as those
previously described. The processor 105 can process such signals to
determine the other parameters. Further, the processor 105 can
store the parameters locally, within the processor 105 and/or a
computer readable storage medium (not shown) communicatively linked
to the processor 105, and/or communicate the parameters to one or
more other systems, as will be described.
[0049] FIG. 3 is a block diagram illustrating an example of a data
processing environment (hereinafter "environment") 300. The
environment 300 can include the smart garment 200 of FIG. 2, and
may include one or more additional smart garments. The environment
300 also can include one or more servers 310, one or more client
devices 320 and one or more receivers (e.g., transceivers) 330. The
client device(s) 320 and RF receiver(s) 330 can be communicatively
liked to the server(s) 310 via a communication network 340.
[0050] The communication network 340 is the medium used to provide
communications links between various devices and systems connected
together within the environment 300. The communication network 340
may include connections, such as wire, wireless communication
links, or fiber optic cables. The communication network 340 can be
implemented as, or include, any of a variety of different
communication technologies such as a WAN, a LAN, a wireless
network, a mobile network, a Virtual Private Network (VPN), the
Internet, the Public Switched Telephone Network (PSTN), or similar
technologies.
[0051] The server 310 can include a garment application 350
executable by one or more processors of the server 310, and store
user profiles 360 for various users, including a user 365 of the
smart garment 200 (e.g., a person who wears the smart garment 200).
The server 310 can store the user profiles 360 locally or on one or
more computer-readable storage devices communicatively linked to
the server 310. The garment application 350 can host a user
interface in which users interact with the server 310 via the
client device(s) 320, or interface with one or more mobile
application via which the users interact with the server 310.
[0052] Each RF receiver 330 can be configured to send and receive
RF signals communicated in accordance with one more suitable RF
communication protocols. For example, the RF receiver(s) 330 can
communicate in accordance with one or more of the IEEE 802-15
communication standards and/or near field communication (NFC). The
RF receiver(s) 330 can receive garment data 370 from the
transmitter 110 of the smart garment 200, as well as receive
garment data from transmitters from other smart garments. The
garment data 370 can include data generated by the sensors 115. By
way of example, at least one RF receiver 330 can be located in a
residence of the user 365, in a medical care facility (e.g., a
hospital, a doctor's office, a diagnostic center, etc.). In one
aspect, multiple receivers 325 can be located in the user's place
of residence and/or multiple receivers 325 can be located in a
medical care facility. For instance, an RF receiver 330 can be
located in a room of user's place of residence, an RF receiver 330
can be located in a room or area of the medical care facility, and
so on. The client device(s) 320 and RF receiver(s) 330 also can be
communicatively linked to the server(s) 310 via the communication
network 340.
[0053] The garment application 350 can receive sensor data
generated by the sensors 115 of the smart garments 200 as garment
data 370. The garment application 350 can receive the garment data
370 via the RF receiver(s) 330 and the communication network 340.
In response to receiving the garment data 370, the garment
application 350 can process the garment data 370 and, based on such
processing, make any of a myriad of determinations regarding the
user 365 and/or the smart garment 200. Further, the garment
application 350 can communicate to the client device(s) 320
information 380 indicating the garment data 370, results of the
determinations regarding the user 365 based on the garment data 370
and/or any other information.
[0054] In one arrangement, the garment application 350 can receive
the garment data 370 each time a smart garment 200 is worn by a
user 365. The garment data 370 for each instance of the smart
garment 200 being worn can indicate a level of stretch in one or
more portions of the smart garment 200. The garment application 350
can compare the garment data 370 from each instance of the smart
garment 200 being worn by the user 365 to the garment data 370
generated from previous instances of the smart garment 200 being
worn by the user 365. Based on such comparison the garment
application 350 can determine at least one size change parameter
indicating at least one change in a size of the user 365. Based on
the size change parameter, the garment application 350 can
determine whether a size of the smart garment 200 is not suitable
for the smart garment 200 to be worn by the user 365 (e.g., the
smart garment 200 is too small or too large for the user 365).
Responsive to determining that size of the smart garment 200 is not
suitable for the garment to be worn by the user 365, the garment
application 350 can output an indication that the size of the smart
garment is not suitable for the smart garment to be worn by the
user 365. For example, the garment application 350 can output the
indication in information 380 by communicating the information 380
to one or more client devices 320. The client device 320 can be a
client device of the user 365 and/or a client device of a care
giver of the user 365.
[0055] In another arrangement, the garment data 370 generated by a
particular smart garment 200 can indicate whether a size of the
smart garment 200 is not suitable for the smart garment 200 to be
worn by the user 365. For example, rather than the garment
application 350 comparing data generated by the sensors 115 for
various instances of the smart garment 200 being worn, the
processor 105 of the smart garment 200 can perform such comparisons
to determine at least one size change parameter indicating at least
one change in a size of the user 365, and include in the garment
data 370 information indicating whether the size of the smart
garment 200 is suitable for the smart garment 200 to be worn by the
user 365. In such an arrangement, the processor 105 can communicate
the garment data 370 to the garment application 350 which, in turn,
can output information 380 based on the garment data 370. In a
further arrangement, the processor 105 can communicate the garment
data 370 directly to the client device(s) 320 via the RF receiver
330 and the communication network 340. In this regard, the
processor 105 can output an indication that the size of the smart
garment 200 is not suitable for the smart garment 200 to be worn by
the user 365 by communicating the information 380 directly to the
client device(s) 320.
[0056] The size of the smart garment 200 can be considered to be
suitable to be worn by the user 365 if data indicating a stretch of
the smart garment 200 when worn by the user 365 is below a
threshold value, or is between a minimum threshold value and a
maximum threshold value. Such data can be garment data 370
generated by sensors 115 in any portion of the smart garment 200,
an average of garment data 370 generated by sensors 115 in a
plurality of portions of the smart garment 200, and/or data
generated by processing garment data 370 generated by sensors 115
in a plurality of portions of the smart garment 200.
[0057] In illustration, the garment application 350 (or the
processor 105) can apply a weighting factor to the sensor data
generated by the sensors 115 based on the locations of the sensors
115. The garment application 350 (or the processor 105) can specify
the weighting factor. For example, the garment application 350 can
specify a greater weight (e.g., a weighting value above a threshold
value) to garment data 370 generated by sensors in a first portion
of the smart garment 200 (e.g., in a portion of the smart garment
200 that covers a user's abdomen) and specify a lesser weight
(e.g., a weighting value below a threshold value) to garment data
370 generated by sensors in a second portion of the smart garment
200 (e.g., in a portion of the smart garment 200 that covers a
user's bicep).
[0058] For example, the garment data 370 can include data generated
by the sensors 115 indicating a level of stretch of the smart
fabric from which the smart garment 200 is made. If the level of
stretch is below a first threshold value, the garment application
350 can determine that the size of the smart garment 200 is too
large for the user 365. If the level of stretch is above a second
threshold value, the garment application 350 can determine that the
size of the smart garment 200 is too small for the user 365. If the
level of stretch is between the first threshold value and the
second threshold value, the garment application 350 can determine
that the size of the smart garment 200 is suitable for the user
365.
[0059] In one aspect of the present arrangements, the garment
application 350 (or processor 105) can, based on the garment data
370, determine a garment size that is suitable for the user 365. In
illustration, the garment data 370 can indicate a particular smart
garment 200, a size of the smart garment 200, and a level of
stretch in one or more portions of the smart garment 200 when the
smart garment 200 is worn by the user 365. The garment application
350 (or processor 105) can, based on the size of the smart garment
200 and the level of stretch in one or more portions of the smart
garment 200 when the smart garment 200 is worn by the user 365,
determine a suitable size of the garment for the user. The garment
application 350 (or processor 105) can indicate such suitable size
in the information 380 communicated to the client device(s) 320.
Such indication can be provided responsive to the garment
application 350 (or processor 105) determining that size of the
smart garment 200 is not suitable for the garment to be worn by the
user 365.
[0060] In a further aspect of the present arrangements, the garment
application 350 (or processor 105) can determine one or more other
particular garments that are different from the smart garment 200.
For example, the other garments can be different styles and/or
different types from the smart garment 200, and may be different in
size from the smart garment 200. Responsive to determining that
size of the smart garment 200 is not suitable for the smart garment
200 to be worn by the user 365, the garment application 350 (or
processor 105) can output an indication of a particular garment
having a garment size that is suitable for the user 365 in the
information 380 communicated to the client device(s) 320. Such
indication can be provided responsive to the garment application
350 (or processor 105) determining that size of the smart garment
200 is not suitable for the garment to be worn by the user 365.
[0061] In other aspects of the present arrangements, the garment
data 370 can include biometric parameters indicating a health of
the user 365.
[0062] In illustration, one or more portions of the sensors 115 can
be configured to measure biometric parameters of the user 365. The
biometric parameters can include, but are not limited to, a body
temperature of the user 365, a heart rate of the user 365, a
respiration rate of the user 365, a level of perspiration rate of
the user 365, a blood pressure of the user 365, a blood sugar level
of the user 365, and so on. In such an arrangement, the smart
garment 200 can include sensors 115 configured to detect body
temperatures of the user 365, respiration rates of the user 365,
perspiration rates (e.g., levels of body moisture) of the user 365,
blood pressure of the user 365, blood sugar levels of the user 365,
etc. In some arrangements, the smart garment 200 may include
additional devices in addition to the sensors 115, transmitter 110
and processor 105. For example, the smart garment may include an
inflatable cuff controllable by the processor 105 to inflate and
deflate for blood pressure measurements in accordance with known
blood pressure measurement techniques. In one aspect of the present
arrangements, the smart garment 200 can be a gown worn by the user
365 while in a medical care facility.
[0063] The garment application 350 can analyze the biometric
parameters and, based on such analysis, provide recommendations to
the user 365 or a care giver of the user 365 in the information 380
communicated to the client device 320. For example, based on
analyzing the biometric parameters, the garment application 350 can
determine patterns in the biometric parameters (e.g., medical
patterns). Based on the determined patterns, the garment
application 350 can determine the recommendations. In illustration,
the information 380 can include a recommendation for the user to
visit a health care facility and/or a medical practitioner, a
recommendation for the user to rest, a recommendation for the user
to perform exercises, a recommendation for the user to take
medication, and so on. In this regard, the information 380 can
include recommendations for the user to take at least one action to
improve the user's health.
[0064] Further, the garment application 350 can analyze the
biometric parameters, as well as other parameters (e.g., determined
size change parameters indicating weight gain or weight loss) and,
based on such analysis, determine a risk of disease for the user
365. For example, the garment application 350 can determine a risk
of the user 365 having one or more diseases. In another example,
the garment application 350 can determine a risk that user 365 may
contract one or more diseases. The garment application 350 also can
determine whether any such risks exceed a threshold value. If so,
the garment application 350 can generate one or more notifications
regarding such risks, and communicate the notifications to the
client device(s) 320 in the information 380. The notifications can
be presented to the user 365 or a care giver of the user 365.
[0065] By way of example, as noted, based on the sensor data the
garment application 350 can determine size change parameters
indicating a change in size of the user 365. The size change
parameters can indicate a particular portion of the user's body
that has changed in size, for example in the abdominal region. If
the size of the abdominal region has increased, this can indicate a
weight gain. The amount of tension measured by sensors 115 that
detect tension, along with the size of the smart garment 200, can
indicate a body mass index for the user 365, which the garment
application 350 can determine based on processing the garment data
370. Based on the body mass index of the user 365 and various
biometric parameters generated by the sensors 115, for example
blood pressure, heart rate and/or respiration rate, the garment
application 350 can determine a risk of the user 365 having a heart
disease.
[0066] To perform the described analyses of the various parameters,
the garment application 350 can interface with one or more medical
databases, which may be components of the server(s) 310 or a
components of external resources (not shown), and access medical
data that correlates symptoms to diseases, health recommendations,
etc. The garment application 350 can analyze the various parameters
using the medical data to arrive at various medically related
determinations described herein. Further, the garment application
350 can perform predictive analysis, which will be described
herein, to predict symptoms indicated by various parameters. In
illustration, an increase in the size of the user's abdominal
region may be due to increased obesity, but may be due to other
factors, for example pregnancy. Using predictive analysis, the
garment application 350 can predict symptoms that cause the
changes, and use such predictions when analyzing the various
parameters to arrive at the various determinations.
[0067] Moreover, the garment application 350 can be configured to,
based on the biometric parameters indicated in the garment data
370, determine at least one health change parameter indicating at
least one change in the state of health of the user 365. For
example, the garment application 350 can monitor, over time,
various biometric parameters for the user 365 indicated in the
garment data 370. Based on such monitoring, the garment application
350 can identify changes in the biometric parameters over time.
Based on the at least one health change parameter indicating the at
least one change in the state of health of the user 365, the
garment application 350 can determine whether the at least one
change in the state of health of the user exceeds a threshold
value. If so, in response, the garment application 350 can output
an indication that the at least one change in the state of health
of the user 365 exceeds the threshold value, for example in the
information 380 communicated to the client device(s) 320.
[0068] By way of example, at least a portion of the sensors 115 can
be configured to detect a respiration rate and/or heart rate of the
user 365 and communicate corresponding biometric parameters to the
processor 105. The processor 105 can be configured to monitor and
process such biometric parameters. Responsive to identifying, based
on processing the biometric parameters, a change in the user's
respiration rate and/or heart rate that exceeds a threshold value,
for example within a predetermined period of time, the processor
105 can output an indication that the at least one change in the
state of health of the user exceeds the threshold value. In one
aspect, the processor 105 can output the indication in garment data
370 communicated to the garment application 350. In response, the
garment application 350 can communicate information 380 to one or
more client device(s) 320 alerting persons, who may or may not
include the user 365, of the indication that the at least one
change in the state of health of the user exceeds the threshold
value. In another aspect, the processor 105 can communicate the
information 380 directly to the client device(s) to alert persons,
who may or may not include the user 365, of the indication that the
at least one change in the state of health of the user exceeds the
threshold value. It should be noted that the present arrangements
are not limited to respiration rate and heart rate, and the above
processes also can be applied to biometric parameters indicating
the user's temperature, blood pressure, or any other biometric
parameters the sensors 115 may detect.
[0069] In one arrangement, outputting the indication that the at
least one change in the state of health of the user 365 exceeds the
threshold value can include generating an alert. In illustration,
information 380 communicated to the client device(s) 320 can
include an alert. Moreover, an alert can be communicated to various
other devices, for example output audio transducers (e.g.,
loudspeakers) configured to propagate audio alert signals,
indication lights configured to propagate visual alert signals, and
so on. Further, the information 380 can include recommendations for
the user to take at least one action to improve the user's health.
For example, the information 380 can include recommendations to
take actions predicted to mitigate health risks resulting from the
change in the state of health of the user 365. In illustration, if
the user's heart rate is above a threshold level, the garment
application 350 can generate a recommendation for the user to rest,
to take medication and/or to seek medical attention. Still, the
information 380 can include any of a myriad of recommendations
based on the change in the state of health of the user 365, and the
present arrangements are not limited in this regard.
[0070] The determination of the information 380 also can be based
on any of a myriad of data from the user profiles 360 and other
data. For example, the information can be based on the user
profiles 360, environmental data, fabric and clothing data, and so
on. The user profiles 360 can include user data including, but not
limited to, name, age body measurements, biometric data, activity
data (including physical activity), size and material preferences,
clothing feedback, family health history (including likelihood of
developing diseases), health history (including allergy history),
etc. The environmental data can include weather data, allergen
information (including allergy tracking, pollen count and pollen
forecast), ambient temperature, etc. The fabric and clothing data
can include material, dimensions, weight, thickness, color,
density, state (dry, wet, stretching data etc.), clothing article
identifier and/or serial number, and so on.
[0071] FIG. 4 is a block diagram illustrating example architecture
for a server 310. The server 310 can include at least one processor
405 (e.g., a central processing unit) coupled to memory elements
410 through a system bus 415 or other suitable circuitry. As such,
the server 310 can store program code within the memory elements
410. The processor 405 can execute the program code accessed from
the memory elements 410 via the system bus 415. It should be
appreciated that the server 310 can be implemented in the form of
any system including a processor and memory that is capable of
performing the functions and/or operations described within this
specification.
[0072] The memory elements 410 can include one or more physical
memory devices such as, for example, local memory 420 and one or
more bulk storage devices 425. Local memory 420 refers to random
access memory (RAM) or other non-persistent memory device(s)
generally used during actual execution of the program code. The
bulk storage device(s) 425 can be implemented as a hard disk drive
(HDD), solid state drive (SSD), or other persistent data storage
device. The server 310 also can include one or more cache memories
(not shown) that provide temporary storage of at least some program
code in order to reduce the number of times program code must be
retrieved from the bulk storage device 425 during execution.
[0073] One or more network adapters 430 can be coupled to server
310 via the system bus 415 to enable the server 310 to become
coupled to other systems, computer systems, remote printers, and/or
remote storage devices through intervening private or public
networks. Modems, cable modems, transceivers, and Ethernet cards
are examples of different types of network adapters 430 that can be
used with the server 310.
[0074] As pictured in FIG. 4, the memory elements 410 can store the
components of the server 310, namely an operating system 435, the
garment application 350 and the user profiles 360. Being
implemented in the form of executable program code, the operating
system 435 and the garment application 350 can be executed by the
server 310 and, as such, can be considered part of the server 310.
Moreover, the operating system 435, the garment application 350 and
the user profiles 360 are functional data structures that impart
functionality when employed as part of the server 310.
[0075] The garment application 350 can include various components,
for example, a delta detector 440, a predictive analyzer 445, a
prescriptive analyzer 450 and a cognitive filter 455. The delta
detector 440, predictive analyzer 445, prescriptive analyzer 450
and a cognitive filter 455 also are functional data structures that
impart functionality when employed as part of the server 310.
[0076] The delta detector 440 can, based various parameters
generated by the sensors 115 of FIG. 3, detect and record changes
in the states of smart garments 200 and/or biometric states of the
user. Examples of the changes in state include, but are not limited
to, stretching of the smart garment 200 when worn, body temperature
changes of the user, heart rate changes of the user, perspiration
rate changes of the user, blood pressure changes of the user, blood
sugar level changes of the user, and so on. The delta detector 440
also can measure levels of changes in the states, and record such
levels as delta values. Moreover, the delta detector 400 can record
with the detected changes and the determined delta values an
indication parameters indicating where on the smart garment 200 the
sensors 115 are located that generated the sensor data used to
determine the detected changes and the determined delta values.
Such parameters can facilitate analysis of the sensor data. For
example, a person who is exercising and sweating may sweat more
profusely in certain areas, and those areas may be known to have a
higher temperature and moisture than other areas during exercise.
Accordingly, when analyzing the data, the garment application 350
can predict that the increased moisture and temperature in those
areas are a result of exercising instead of a being due to a
medical issue. In another example, if a person is walking in the
rain, it is more likely that they will have moisture on their
shoulders, chest area and back than other areas of the body. If,
when analyzing the data, the garment application 350 detects a
higher level of moisture in those areas, and the garment
application 350 accesses data indicating rainy weather conditions,
the garment application 350 can predict that the moisture is due to
rain rather than being due to a medical issue.
[0077] As noted, the garment application 350 can detect different
levels of tension (e.g., stretch) in different areas of the smart
garment 200. Further, the delta detector 440 can detect a
difference in tension, or stretch, between when the garment is worn
and the garment is not worn, and record corresponding delta values
for various regions of the smart garment 200. The delta detector
400 also can determine additional delta values representing the
change in the tension over time, for example as the user gains or
loses weight. The garment application 350 can analyze the various
delta values to detect and learn about changes in the user's body
over time. Such changes can be considered by the garment
application 350 when performing various analyses, such as those
previously described with respect to FIG. 3.
[0078] The predictive analyzer 445 can process sensor data received
from the sensors 115 in the garment data 370 to help determine the
previously described risk of disease. For example, the waist size
of the user can be a parameter that is analyzed to predict
accurately the user's risk of experiencing heart disease. As noted,
however, a person's waist size can vary for various reasons, and
the predictive analyzer 445 can process waist size parameters along
with various other detected and/or generated parameters to predict
a cause of the change in waist size. For example, delta values
corresponding to tension around the waste can indicate in increase
in waist size corresponding to pregnancy, rather than obesity.
Using predictive analytics, the predictive analyzer 445 can
identify such circumstance. The garment application 350 can use the
results of the prediction when determine various medical analyses
for the user, such as those previously described. Moreover, as the
delta detector 440 generated delta values over time, the predictive
analyzer 445 can update various predictions for the user.
[0079] The prescriptive analyzer 450 can determine various
recommendations for the user based on the sensor data generated by
the sensors 115, data generated by the delta detector 440, and data
generated by the predictive analyzer 445. As noted, examples of
such recommendations can include a recommendation to see a medical
professional, a recommendation to increase or decrease levels of
physical activity, etc. Other examples include a recommendation to
increase or decrease caloric, nutritional or supplemental indicate,
a recommendation to purchase clothing in a different size,
recommendation to purchase clothing using a different fabric or
material, etc. Because the user's data may change of time, the
prescriptive analyzer 450 can update recommendations, but also may
re-prescribe other recommendations that still are relevant to the
user.
[0080] The cognitive filter 455 can implement cognitive analysis,
which is known in the art, to track garment data 370 from various
smart garments 200 worn or purchased by the user, and learn the
user's clothing preferences. Further, the cognitive filter 455 can
learn which sizes of clothing fit the user for various brands. For
example, the user may wear size 4 in clothing from a first
manufacturer, but wear size 6 in clothing from a second
manufacturer, and the cognitive filter 455 can learn and store
corresponding data. Still, the cognitive filter 455 can perform
various other types of cognitive analysis, and the present
arrangements are not limited in this regard.
[0081] FIG. 5 is a flow chart illustrating an example of a method
500 of outputting a notification regarding a size of a smart
garment. The method 500 can be implemented by the server 310 (e.g.,
the garment application 350) or the smart garment 200 (e.g., the
processor 105) of FIG. 1. The following description discusses
implementation of the method 500 by the garment application 350,
but it will be understood that the processor 105 can implement the
method 500 using suitable configured computer program code.
[0082] At step 502, the garment application 350 can receive sensor
data generated by at least a first portion of a plurality of
sensors integrated into a smart garment, the sensor data indicating
a level at which fabric of the smart garment is stretched when worn
by a user. At step 504, the garment application 350 can determine,
using a processor, based on the sensor data, at least one size
change parameter indicating at least one change in a size of the
user. At step 506, the garment application 350 can, based on the at
least one size change parameter indicating the at least one change
in the size of the user, determine whether a size of the smart
garment is not suitable for the smart garment to be worn by the
user. At step 508, the garment application 350 can, responsive to
determining that size of the smart garment is not suitable for the
smart garment to be worn by the user, output a notification
indicating that the size of the smart garment is not suitable for
the smart garment to be worn by the user. At step 510, the garment
application 350 can, responsive to determining that size of the
smart garment is not suitable for the garment to be worn by the
user, output another notification indicating a garment size that is
suitable for the user. At step 512, the garment application 350
can, responsive to determining that size of the smart garment is
not suitable for the garment to be worn by the user, output another
notification indicating a particular garment having a garment size
that is suitable for the user.
[0083] FIG. 6 is a flow chart illustrating an example of a method
600 of outputting a notification regarding a risk of disease for a
user. The method 600 can be implemented by the server 310 (e.g.,
the garment application 350) of FIG. 1.
[0084] At step 602, the garment application 350 can receive sensor
data generated by at least a portion of a plurality of sensors
integrated into a smart garment, the sensor data indicating at
least one biometric parameter indicating a state of health of a
user. At step 604, the garment application 350 can determine, based
on the sensor data, a risk of disease for the user. At step 606,
the garment application 350 can determine whether the risk of the
disease for the user exceeds a threshold value. At step 608, the
garment application 350 can, responsive to determining that the
risk of the disease for the user exceeds the threshold value,
output a notification indicating the risk of the disease for the
user.
[0085] FIG. 7 is a flow chart illustrating an example of a method
700 of outputting a notification regarding a change in the state of
health of a user. The method 700 can be implemented by the server
310 (e.g., the garment application 350) of FIG. 1.
[0086] At step 702, the garment application 350 can receive sensor
data generated by at least a portion of the plurality of sensors
integrated into the smart garment, the sensor data indicating at
least one biometric parameter indicating a state of health of a
user. At step 704, the garment application 350 can determine, based
on the sensor data, at least one health change parameter indicating
at least one change in the state of health of the user. At step
706, the garment application 350 can, based on the at least one
health change parameter indicating the at least one change in the
state of health of the user, determine whether the at least one
change in the state of health of the user exceeds a threshold
value. At step 708, the garment application 350 can, responsive to
determining that the at least one change in the state of health of
the user exceeds the threshold value, output a notification
indicating that the at least one change in the state of health of
the user exceeds the threshold value. At step 710, the garment
application 350 can output another notification indicating a
recommendation for the user to take at least one action to mitigate
a health risk resulting from the change in the state of health of
the user.
[0087] While the disclosure concludes with claims defining novel
features, it is believed that the various features described herein
will be better understood from a consideration of the description
in conjunction with the drawings. The process(es), machine(s),
manufacture(s) and any variations thereof described within this
disclosure are provided for purposes of illustration. Any specific
structural and functional details described are not to be
interpreted as limiting, but merely as a basis for the claims and
as a representative basis for teaching one skilled in the art to
variously employ the features described in virtually any
appropriately detailed structure. Further, the terms and phrases
used within this disclosure are not intended to be limiting, but
rather to provide an understandable description of the features
described.
[0088] For purposes of simplicity and clarity of illustration,
elements shown in the figures have not necessarily been drawn to
scale. For example, the dimensions of some of the elements may be
exaggerated relative to other elements for clarity. Further, where
considered appropriate, reference numbers are repeated among the
figures to indicate corresponding, analogous, or like features.
[0089] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0090] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0091] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0092] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0093] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0094] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0095] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0096] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0097] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the invention. As used herein, the singular forms "a," "an," and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "includes," "including," "comprises," and/or
"comprising," when used in this disclosure, specify the presence of
stated features, integers, steps, operations, elements, and/or
components, but do not preclude the presence or addition of one or
more other features, integers, steps, operations, elements,
components, and/or groups thereof.
[0098] Reference throughout this disclosure to "one embodiment,"
"an embodiment," or similar language means that a particular
feature, structure, or characteristic described in connection with
the embodiment is included in at least one embodiment described
within this disclosure. Thus, appearances of the phrases "in one
embodiment," "in an embodiment," and similar language throughout
this disclosure may, but do not necessarily, all refer to the same
embodiment.
[0099] The term "plurality," as used herein, is defined as two or
more than two. The term "another," as used herein, is defined as at
least a second or more. The term "coupled," as used herein, is
defined as connected, whether directly without any intervening
elements or indirectly with one or more intervening elements,
unless otherwise indicated. Two elements also can be coupled
mechanically, electrically, or communicatively linked through a
communication channel, pathway, network, or system. The term
"and/or" as used herein refers to and encompasses any and all
possible combinations of one or more of the associated listed
items. It will also be understood that, although the terms first,
second, etc. may be used herein to describe various elements, these
elements should not be limited by these terms, as these terms are
only used to distinguish one element from another unless stated
otherwise or the context indicates otherwise.
[0100] The term "if" may be construed to mean "when" or "upon" or
"in response to determining" or "in response to detecting,"
depending on the context. Similarly, the phrase "if it is
determined" or "if [a stated condition or event] is detected" may
be construed to mean "upon determining" or "in response to
determining" or "upon detecting [the stated condition or event]" or
"in response to detecting [the stated condition or event],"
depending on the context.
[0101] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
* * * * *