U.S. patent application number 16/120390 was filed with the patent office on 2018-12-27 for training systems with wearable sensors for providing users with feedback.
The applicant listed for this patent is ENFLUX INC.. Invention is credited to Doug Hoang, Elijah J. Schuldt.
Application Number | 20180369637 16/120390 |
Document ID | / |
Family ID | 63294591 |
Filed Date | 2018-12-27 |
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United States Patent
Application |
20180369637 |
Kind Code |
A1 |
Hoang; Doug ; et
al. |
December 27, 2018 |
TRAINING SYSTEMS WITH WEARABLE SENSORS FOR PROVIDING USERS WITH
FEEDBACK
Abstract
A training system based on mobile technology and kinematics of
human motion characterizes, analyzes, and supplies feedback to a
user based on the user's movements. The training system includes a
garment having a sensor control module connected to multiple sensor
nodes via electrically-conductive fabric running along parts
portions of the garment. The sensor module/nodes can communicate
through the conductive fabric. The sensor nodes acquire motion
and/or physiologic readings that are wirelessly transmitted to a
mobile computing device that runs an application that analyzes the
data and provides visual (e.g., graphs, 3D avatar) and audio
feedback (e.g., voice prompts). Vibration motors and
LEDs/electroluminescent fabric in the garment also provide
notifications and alerts. The triple layer of garment, conductive
fabric, and sensor module/sensor node are sealed against
contaminants, allowing the garment to be washable.
Inventors: |
Hoang; Doug; (Cupertino,
CA) ; Schuldt; Elijah J.; (Cupertino, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ENFLUX INC. |
Cupertino |
CA |
US |
|
|
Family ID: |
63294591 |
Appl. No.: |
16/120390 |
Filed: |
September 3, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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14968411 |
Dec 14, 2015 |
10065074 |
|
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16120390 |
|
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62091136 |
Dec 12, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G09B 19/0038 20130101;
A41D 1/04 20130101; G16H 20/30 20180101; A63B 24/0003 20130101;
G01P 1/02 20130101; A63B 2220/40 20130101; A63B 2220/89 20130101;
A63B 24/0062 20130101; A63B 2220/803 20130101; G16H 50/30 20180101;
G08B 5/36 20130101; G09B 19/003 20130101; G16H 40/63 20180101; A41D
1/005 20130101; A41B 1/08 20130101; A63B 2220/83 20130101; A41D
19/0027 20130101 |
International
Class: |
A63B 24/00 20060101
A63B024/00; G01P 1/02 20060101 G01P001/02; G08B 5/36 20060101
G08B005/36; G09B 19/00 20060101 G09B019/00; A41D 19/00 20060101
A41D019/00; A41D 1/04 20060101 A41D001/04; A41D 1/00 20060101
A41D001/00; A41B 1/08 20060101 A41B001/08 |
Claims
1. An electrically conductive fabric, the fabric comprising: a) a
substrate having a conductive material integrated therein, b) a
sensor control module connected to multiple sensor nodes via
electrically-conductive fabric running, wherein the module is
operatively connected to the conductive material, c) one or more
pins that hold the sensor nodes to the substrate, and d) sensor
voids on the surface of the substrate.
2. The fabric as claimed in claim 1, wherein the substrate has an
elastomer that regulates stretch of the fabric.
3. The fabric as claimed in claim 1, wherein the sensor modules is
attached directly to the electrically conductive material.
4. The fabric as claimed in claim 1, wherein the conductive
material is a conductive yarn.
5. The fabric as claimed in claim 1, wherein the fabric has a
thickness from 0.45 mm to 4 mm.
6. A garment system including: a) a garment having a sensor module
secured thereto; b) at least one motion sensor in at least one
sensor node that: 1) is secured to the garment; 2) interfaces with
the sensor module; and 3) acquires motion data; and c) a wireless
transmitter configured to send motion data acquired by the at least
one motion sensor to another computing device wherein the sensor
module is connected to the sensor node via electrically-conductive
fabric running along a portion of the garment.
7. The system of claim 6, wherein: a) the sensor module includes a
module case membrane; and b) the system further includes a module
conducting pin extending through a portion of the module case
membrane to contact the electrically-conductive fabric so as to
allow signals and power to travel from the sensor module via the
electrically-conductive fabric.
8. The system of claim 6, wherein a) further including a first pair
of sensor nodes connected to each other in series, and a second
pair of sensor nodes connected to each other in series; b) wherein
the first pair of sensor nodes and the second pair of sensor nodes
are connected to the sensor module in parallel.
9. The system of claim 6, wherein the real-time feedback includes a
3D avatar representative of a user: a) wearing the garment; and b)
making movements captured as motion data by the at least one motion
sensor.
10. The system of claim 6, wherein: a) the garment includes an LED;
and b) the system is configured to provide feedback to a user
wearing the garment by turning on the LED, the feedback being based
on the motion data.
11. The system of claim 6, wherein: a) the system further includes
at least one of: 1) a 3-axis accelerometer; 2) a 3-axis gyroscope;
and 3) a 3-axis magnetometers; and b) the accelerometer, gyroscope,
and/or magnetometer are packaged into one or more sensor nodes
positioned on one or more limbs of the garment to acquire data
related to orientation.
12. The system of claim 6, further including at least one
physiological sensor for acquiring biometric data from a user
wearing the garment.
13. The system of claim 6, further including an image capture
device having a camera secured to the garment, the image capture
device being configured to capture images as a user wearing the
garment moves.
14. The system of claim 6, wherein: a) the garment is a shirt; and
b) the system includes one or more motion sensors secured to the
garment on at least one of: 1) both a left wrist segment and a
right wrist segment; 2) both a left upper arm segment and a right
upper arm segment; and 3) a torso segment.
15. The system of claim 6, wherein: a) the garment is a pair of
pants; and b) the system includes one or more motion sensors
secured to the garment on at least one of: 1) a hip segment; 2)
both a left thigh portion and a right thigh segment; 3) both a left
shin portion and a right shin portion.
16. The system of claim 6, wherein: a) the garment is a glove; and
b) the system includes one or more motion sensors secured to the
glove on: 1) each of five finger segments; 2) a palm segment; and
3) a back of the hand segment.
17. The system of claim 6, further including one or more motion
sensors configured to be secured to a head of a user.
18. The system of claim 6, further including one or more motion
sensors configured to be secured to a foot of a user.
19. A physical training system for fitness or medical applications,
the training system including a set of sensor bands configured to
be secured to a user, each sensor band having: a) at least one
motion sensor; and b) a transmitter configured to send motion data
to another computing device which is configured to provide, based
on the motion data, visual feedback including at least one of: 1) a
chart or graph depicting a quantity or quality of motions; and 2)
an avatar that simulates movements representing actual or idealized
movements of a user.
20. The system of claim 19, further including a garment having a
sensor node secured thereto, the sensor node: a) having at least
one motion sensor for acquiring motion data; and b) being
configured to send motion data: 1) to a module secured to the
garment via a physical connection; or 2) to another computing
device not secured to the garment via a wireless connection.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of application Ser. No.
14/968,411, filed Dec. 14, 2015, which claims priority to U.S.
Provisional Patent Application 62/091,136 filed Dec. 12, 2014, the
entirety of which is incorporated by reference herein.
FIELD OF THE INVENTION
[0002] This document concerns an invention relating generally to
systems and methods of measuring, reporting, and guiding
performance related to motion, posture, and form during athletic or
medical-related movements and activities using wearable personal
sensors that wirelessly communicate with computing devices and
provide feedback.
BACKGROUND OF THE INVENTION
[0003] Traditionally, a user seeking feedback on his or her
posture, movement, and technique during exercise, or while playing
sports, would employ a trainer or coach to observe the user and
provide feedback on his or her movements. The coach or trainer
would often set up a camera to video record the user's movements in
a specific environment for subsequent review. But this setup
process can be tedious and the camera equipment can be very
expensive, and hiring a trainer or coach can be very costly. Also,
analysis of a video replay of a movement does not allow for
real-time feedback. Moreover, even experienced trainers and coaches
can miss day-to-day differences, incremental changes, and small
errors that, at least over time, can lead to errors,
inefficiencies, and/or injuries. What is needed is a system that
can be used in a variety of places without time consuming or
expensive setup processes, and that can provide precise real-time
feedback.
SUMMARY OF THE INVENTION
[0004] The invention, which is defined by the claims set forth at
the end of this document, is directed to training systems which at
least partially alleviate the aforementioned problems. A basic
understanding of some of the features of preferred versions of the
invention can be attained from a review of the following brief
summary of the invention, with more details being provided
elsewhere in this document. To assist in the reader's
understanding, the following review makes reference to the
accompanying drawings (which are briefly reviewed in the "Brief
Description of the Drawings" section following this Summary section
of this document).
[0005] Exemplary versions of the invention enable people to enhance
or maximize the benefits of time spent exercising at a gymnasium or
elsewhere, reduce injuries, train more optimally for sports and
other athletic movements, and evaluate and guide movements for
rehabilitation or other medical reasons. One or more garments (such
as gym shirts and pants) with sensors woven into the fabric thereof
acquire data on motion to analyze full body form during athletic
and other movements. The garments with sensors can connect and
transmit movement data to a wireless-enabled computing device
having a software application that can provide real-time visual and
audio feedback during and after exercise routines. The feedback
allows the user to better understand inefficiencies, and improve
their technique in order to reduce or avoid injuries and achieve
performance and fitness goals. The system can help a user correct
form and enhance technique for a variety of sports/athletic and
medical movements and activities, such as weight lifting, CrossFit,
yoga, Pilates, karate, tai chi, boxing, mixed martial arts, Aikido,
taekwondo, basketball, golf, tennis, baseball, bodybuilding,
cricket, football, gymnastics, rowing, crew, lacrosse, hockey,
field hockey, fencing, rugby, skiing, snowboarding, surfing,
soccer, squash, swimming, tennis, volleyball, wrestling, diving,
figure skating, ice skating, dancing, track and field, sprinting,
throwing, jumping, long jumping, triple jumping, pole vaulting,
discus, shot put, javelin, hammer, cycling, long distance running,
triathlons, hurdling, table tennis, pool, darts, archery,
badminton, horseback riding, horse racing, auto racing, physical
therapy, rehabilitation from injuries, rehabilitation from
surgeries and medial operations, healthcare applications, etc.
[0006] Exemplary versions can be applied and/or adapted for such
other applications as video gaming, augmented reality, virtual
reality, etc., to provide high accuracy devices able to track
motion in simulated situations. For example, exemplary versions can
be used with the Oculus Rift and other devices in augmented or
virtual reality markets to provide new and novel experiences for
the user by submerging them further into the simulated application.
Moreover, personal trainers or coaching figures can enhance their
training and coaching of users by basing efforts on more precise
and accurate data. For example, a coach or personal training figure
could use the training system for insights into the user's
movements and to track the user's progress by evaluating patterns
and trends. The system can also provide notifications (e.g., via
text message, email, in-app and out-of-app messages, etc.) to
highlight critical information related to the user's performance
and progress. Data on user movement, performance, and progress
during exercise, training, therapy, and rehabilitation can also be
collected for anonymous big data analytics to gain insights into
how athletes train, improve, heal, etc. These and other
applications and markets benefit from valuable insight into the
human body provided by the disclosed system.
[0007] In general terms and without limiting scope, exemplary
versions of the invention will be discussed in the context of five
main components, as outlined here.
[0008] (1) Garment: these include tops such as shirts, bottoms such
as pants or shorts, accessories such as gloves, etc.
[0009] (2) Sensor Nodes: these typically house motion sensors and
transmit unprocessed motion data to sensor modules and attach
directly to the electrically conductive fabric of a garment. They
serve as wearable personal sensing devices.
[0010] (3) Sensor Bands: these are sensors that share the same
electrical componentry as the sensor node and the sensor module.
These devices do not attach through the conductive fabric. These
are particularly useful for users who prefer short sleeve shirts
and shorts instead of long sleeves and pants. This component also
serves as a personal sensing device.
[0011] (4) Sensor Module: typically, one garment (shirt, pants,
etc.) has one sensor module, so an outfit having one shirt and one
pair of pants would have two sensor modules, one for the "top," and
one for the "bottom." This is typically the main processing unit
that processes the sensor data and transmits wirelessly to a
computing device that has a Graphical User Interface (GUI). They
serve as a wearable personal sensing device.
[0012] (5) Electrically Conductive Fabric: this connects the sensor
nodes to the sensor modules.
[0013] These components allow the system to capture full-body form
motion of athletic and other movements, and send information
wirelessly to a computing device with a GUI to give the user
real-time feedback on movements. The motion sensors that are
preferably used include a 3-axis accelerometer, 3-axis
magnetometer, and 3-axis gyroscope (together, the accelerometer,
magnetometer, and gyroscope, or AMG), coupled with data fusion
algorithms, an extended Kalman filter (EKF), and an attitude
heading and reference system (AHRS) to gather the raw data from the
AMG and process it into movement orientation. The AMG is positioned
to measure each major rigid limb of the body (arms, torso, and
legs), for a total of (for example) 10 sensors in preferable
versions. This provides full-body movement form measurement and
analysis, not achieved by prior systems of comparable cost and
mobility.
[0014] The "smart" garment/clothing include miniaturized motion
sensors--such as, for example, microelectromechanical systems
(MEMS) packages--that are integrated at multiple strategic
positions in the clothing. The sensor node(s), sensor module(s),
and electrically conductive fabric are sufficiently small such that
the components provide an aesthetically pleasing, ergonomic, and
unique user experience. Due to the size of the components, the
fabric is very breathable and very stretchable, resulting in a very
comfortable user experience. The electrical components and sensor
integration into the garment are designed to withstand multiple
machine wash and dry cycles. Exemplary methods (further discussed
below) of integrating sensors and electrically conductive fabric
into the garment methods (such as triple layering and sensor
penetration) achieve both durability and comfort. Exemplary
versions of the smart garment can be impervious to sweat and
water.
[0015] Signal processing and error filtering techniques related to
data fusion can be used in extracting orientation data from
accelerometers, gyroscopes, and magnetometers. Software in the
system enhances the sensor accuracy and reduces calibration
routines, avoiding delays and unnecessary steps involved in
receiving feedback from the product.
[0016] The training system can integrate video capturing
capabilities into the architecture. The video capturing capability
can be provided by the wireless-enabled mobile computing device
with a camera, or any dedicated capturing device that is able to
connect to the training system to transmit and receive tasks,
services, commands, or a combination thereof. Video capturing
capabilities would synchronize with motion and other data so as to
provide additional information to the user, enhancing form analysis
and feedback. Coupling biometric and motion data preferably invokes
suitable external software tasks, processes, and services to
provide the user with an ergonomic and easy to understand
interface. The video may record in any frame rate which allows a
user to understand the kinematics of human motion in the respective
usage application.
[0017] A user can begin by wearing the garment(s) and starting to
exercise, play sports, or otherwise move at (for example) a gym,
outside in a field, under water in a pool or lake, at a clinic, or
elsewhere. The exercise can involve (for example) weight lifting
equipment (such as barbells, weight machines, dumbbells, kettle
balls, or other free weights), balls, etc. The exercise can also
involve other equipment, such as a baseball bat, a golf club, a
javelin, a discus, a shotput, etc. Optionally, a ball, bat, club
(or other equipment) can be equipped with its own motion sensor to
allow for analysis of the equipment's motion simultaneously with
analysis of the user's body motion. The end user would preferably
use a wireless-enabled computing device, such as a smart phone or
other suitable mobile computing device (such as a tablet, notebook,
laptop, smart watch, etc.) with wireless communications technology
(such as Wi-Fi, Bluetooth, etc.) for communicating with the
circuitry of the garment and running application software.
Networking capabilities (such as an internet connection or
local/wide area network connections) can further be used to enhance
post processing capabilities. The code related to user feedback can
be stored and processed on the wireless-enabled computing device,
or it can be stored and executed on a cloud server or remote
stationary computing device, or it can be handled onboard the
sensor modules, or any combination thereof. The user receives
feedback regarding his or her motions, helping the user be more
efficient and effective, and reducing the risk of injuries from
improper form or technique.
[0018] The garment (or "smart" clothing) is preferably machine
washable. The garment uses a combination of textile coatings, such
as silicone, and highly stiff materials, such as polycarbonate and
brass, to provide robust and structural bonds for protection from
harmful contaminants which may damage sensitive electronic
components. Sensitive components, such as electrically conductive
yarns or materials and the electronic circuitry are protected
against impact and also hermetically sealed through such coatings
and bonding of highly stiff materials. The product form factor and
care techniques are similar to compression athletic garment, like
Under Armour or Lululemon compression offerings (for example,
machine wash on cold and dry in dryer). The garments can also be
washed by hand and hang dried for increased life. Standard washing
solvents and household chemicals can be used to wash the garments,
which are expected to last multiple machine wash and dry
cycles.
[0019] Further advantages and features of the invention will be
apparent from the remainder of this document in conjunction with
the associated drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] FIG. 1 represents an exemplary feedback process of exemplary
training systems.
[0021] FIG. 2 is an exemplary virtual representation of a human for
displaying ideal movement, technique, and/or posture for emulation
by a user.
[0022] FIG. 3 represents an exemplary magnetic field disturbance
compensation technique involving an extended Kalman filter and
magnetometer channel measurements.
[0023] FIG. 4 is an exemplary adaptive extended Kalman filter
process.
[0024] FIG. 5 is an exemplary flowchart for data processing.
[0025] FIG. 6 shows an exemplary avatar in a "T pose" position.
[0026] FIG. 7 shows exemplary button hole examples.
[0027] FIG. 8 shows a sensor node case membrane.
[0028] FIGS. 9-18 depict alternative exemplary versions of a sensor
node electronic circuitry layout.
[0029] FIG. 19 shows an exemplary layout for a sensor band.
[0030] FIG. 20 depicts assembly of an exemplary sensor band.
[0031] FIG. 21 depicts an exemplary interaction between sensor
nodes and sensor modules.
[0032] FIG. 22 shows a holster and brain(s) of an exemplary sensor
module.
[0033] FIG. 23 depicts exemplary garment exemplary positions for
sensor nodes and sensor module.
[0034] FIGS. 24-30 depict alternative exemplary versions of a
sensor module and sensor band electronic circuitry layout.
[0035] FIG. 31 represents a top view of electrically conductive
fabric, in which the four horizontal details are the conductive
fabric. The distance of 2.3 mm is the distance between each
yarn.
[0036] FIGS. 32 and 33 show alternative exemplary assembly
processes.
[0037] FIG. 34 represents an exemplary sensor void.
[0038] FIG. 35 depicts an exemplary sensor node or exemplary sensor
module installed in an electrically conductive fabric.
DETAILED DESCRIPTION OF PREFERRED VERSIONS OF THE INVENTION
[0039] Referring initially to FIG. 1, an exemplary training system
uses a variety of components and configurations to provide a method
for characterizing, analyzing, and supplying a user real-time
feedback on various performance metrics related to athletic and
medical related movements. For example, at least some intelligent
automated health and fitness analysis system(s) may be configured,
designed, and/or operable to provide various different types of
operations, functionalities, and/or features, such as one or more
of the following: (i) automate calculation, detection, and input of
data relating to exercise movement, such as (for example)
repetitions, set completion, exercise completion, movement
completion, range of motion, power, eccentric and concentric phase,
balance, heart rate, caloric expenditure, tempo, acceleration,
velocity, position, gamification score, form efficiency, rest time,
distance traveled, force of impact, and 3D avatar movement; in
addition to automating the process of using these data and
services, the training system can also enable the combined use of
several resources of data and services at once; (ii) automate the
use of data and services available over the training system to
determine and offer personal recommendations to a user to (for
example, but not limited to) select a resistance load, perform a
specified number of repetitions, adjust form and technique (post or
during movement), etc.; (iii) enable the operation of components,
tasks, and services to provide a GUI having, for example, charts,
graphs, and animations to specifically display user performance
metrics and offer personal recommendations. The user performance
metrics can include, but are not limited to: set completion,
exercise completion, movement completion, range of motion, power,
eccentric and concentric phase, balance, heart rate, caloric
expenditure, tempo, acceleration, velocity, position, gamification
score, form efficiency, rest time, distance traveled, force of
impact, and 3D avatar movement. At least a portion of the various
types of functions, operations, actions, and/or other features
provided by exemplary versions of the training system can be
implemented at one or more client system(s), at one or more server
system(s), and/or combinations thereof.
[0040] Body Guidance Component
[0041] A body guidance component is an automated and personalized
workout mechanism to assist the user in attaining an athletic or
medical related goal. The body guidance component compiles and
prepares fitness or medical regimens relating to movements,
including (for example) recommendations related to exercise or
movement, resistance, repetition quantity, set quantity, movement
plan, or a combination of thereof.
[0042] The body guidance component can include different types of
components, modules, processes, systems, and the like, that may be
implemented and/or instantiated using hardware and/or software. The
different software and hardware components include, for example
components related to: estimating and comparing state; using
historical workout performance data; updating; database tracking;
using a database library of athletic or medical movement; active
input elicitation; using short and long term memory; storing data
on a server; math modeling; machine learning modeling related to
exercise repetition, set, and resistance load; using reference
data; muscle affected by exercise modeling; orchestrating services;
task flow modeling; service modeling; and output processing. Goal
data can be collected from the user, and the component can compile
and analyze historic data of (for example) kinematic movement to
determine the progression status of the user with respect to the
goal and to formulate the necessary movement regimen to achieve the
goals. The user can continually update their body geometry to gain
results.
[0043] The body guidance component categorizes goals into domains
and matches movements based on pre-determined and assigned criteria
and classifications based on how certain movements affect the body.
For example, a user with a goal of gaining muscle mass in the chest
segment of the body can utilize the body guidance component to
formulate an exercise routine that focuses on exercises and methods
of movement to target muscle activity in the chest segment. The
body guidance component could formulate an interactive regimen
involving, for example, athletic movements, quantity of
repetitions, quantity of sets, quantity of resistance, with
specifications on set completion, exercise completion, movement
completion, range of motion, power, eccentric and concentric phase,
balance, heart rate, caloric expenditure, tempo, acceleration,
velocity, position, gamification score, form efficiency, rest time,
distance traveled, force of impact, and 3D avatar movement, or a
combination thereof. The body guidance component can continuously
analyze and store performance data related to kinematics of motion,
physiological measurements, or a combination thereof during a
workout. The body guidance component could compare current status
regarding user performance versus performance goals, such as (for
example) the weight of the user or the maximum amount of weight
which can be lifted. Using this information, the system can
prescribe optimal movement regimens to attain goals in a quick and
efficient manner. The historical kinematics of movement and
physiological measurement data for each individual user can be
stored within the cloud for subsequent access by the same or other
devices for body guidance analysis, progression analysis, etc.
[0044] User Interface
[0045] The user interacts with the GUI of the application software
to input necessary information about their body attributes and
their goals. The user interface is responsible for communication of
user input to software algorithms, and computation outputs to the
user. User input includes, for example, athletic movement
selection, closing of tasks, calibration of sensing devices, and
initiation of the software. System outputs include, for example,
notification messages, 3D videos of the exercise being performed
(see FIG. 2), and performance metrics. The system can synchronize
sensing devices, placed on one or more segments of the body, with
the wireless-enabled computing device. The user can then select an
exercise or body posture and perform a certain task. After the task
is completed, the wireless-enabled computing device can provide
feedback in different forms (such as visually through, for example,
graphs and charts, and using sound such as through voice synthesis,
etc.). The user is allowed to input information into the device and
make selections relating to height and weight, body geometry,
exercise, body posture, etc. These can be entered in any manner
deemed suitable, such as through a touchscreen or using voice
commands.
[0046] Connecting
[0047] The user wears garments and (if applicable) separate sensor
bands and connects to the application software being run on the
wireless-enabled computing device. As further discussed below, the
user puts on garments so that sensor nodes and modules are worn in
the correct position with respect to the human body. The user then
opens the application software, which could be an application,
executable, or software task depending on whether the
wireless-enabled computing device is a smart phone, tablet (such as
an iPad), laptop, or a desktop computer (such as an Apple computer,
a Windows-based Personal Computer (PC), etc.). Once it is running,
the software may automatically start searching for the garments and
(if applicable) sensor bands to connect and synchronize a data
transferring connection. The device which has the code base
installed on its own electronic circuitry will start searching and
scanning for recognizable systems through Wi-Fi, Bluetooth,
internet, or other suitable connectivity protocols.
[0048] Garments (and sensor bands) may have unique radio addresses
that are recognized by the application software, helping
distinguish one person's system from another nearby person's
system. This helps avoid or reduce interferences and confusion by
allowing one user's system to recognize and communicate with the
correct hardware. The unique radio addresses may be printed on the
surface of the garment, case membrane, or otherwise made available
to the user via (for example) email or other easily accessible and
distinguishable method. The user could additionally or
alternatively be provided with an "umbrella" code identifier which
would query the cloud based server to automatically populate the
unique device address list.
[0049] The sensor node(s), module(s), and sensor band(s) may have
separate power buttons, or, in preferred versions, only the sensor
module(s) and sensor band(s) have power buttons. Pressing a button
migrates the sensors from a low power state, also known as a
"sleep" state or "sleep mode," to a fully-functional state.
Moreover, the sensor can migrate from a lower power state upon
sensing movement. Once the power button is depressed, the sensor
node(s), module(s), and band(s) begin to advertise their respective
unique radio addresses for recognition by the computing device.
Calibration preferably need only be performed once after initially
wearing the garment(s) and sensor bands (if any) but before the
user starts his or her first workout. Calibration is further
discussed below. Additional calibration techniques for correcting
positioning errors injected into the system during the alignment
process are also discussed below.
[0050] Moving
[0051] Once the garment is connected to the software application,
the user can select pre-defined exercises, athletic movements,
workouts, etc., or create and perform their own exercises,
movements, and workouts. The source code base for selecting
workouts or for selecting or creating exercises can be executed on
the computing device (with the GUI) or on a remote server, such as
the cloud, or on-board the electronic circuitry of the sensor
band(s), sensor node(s), or sensor module(s). The sensor module(s),
sensor node(s), or sensor band(s) could also interface with the
user to allow selection of exercises and navigation through the
GUI. The user starts performing athletic movements, rehabilitation,
or other suitable movements. To help the user progress, the system
provides real-time feedback, examples of which follow.
[0052] (1) Voice Feedback: The wireless-enabled computing device
informs and instructs the user regarding current performance and
opportunities to improve on performance with respect to goals.
[0053] (2) 3D avatar Form Comparison: The computing device shows
the movement of the user and/or idealized examples of movements
using an avatar to instruct the user on how he or she may improve
performance based on the movements performed and/or on the
idealized movements.
[0054] (3) Multicolor Light: LEDs or electroluminescent fabric may
be situated on each limb within electronic circuitry or
electrically conductive fabric to illuminate and draw attention of
the user to feedback, warnings, etc. as part of general
informational notifications and guidance, and/or as part of alerts
related to preventing injuries.
[0055] (4) Tactile Feedback: Vibration motors located in the
garment(s) and (if applicable) the sensor bands can vibrate to
indicate warnings of movement to prevent injuries and/or to provide
general notifications.
[0056] (5) Push notifications: Notifications highlighting critical
information related to the user's performance can also be provided
via, for example, text message, e-mail notifications, in-app
notifications, out-of-app notifications, social media responses,
etc.
[0057] (6) Graphs and/or charts displayed on the screen of the
wirelessly enabled device, providing indicators of user
performance, such as set completion, exercise completion, movement
completion, range of motion, power, eccentric and concentric phase,
balance, heart rate, caloric expenditure, tempo, acceleration,
velocity, position, gamification score, form efficiency, rest time,
distance traveled, force of impact, 3D avatar movement, progression
of performance over time, overall performance, or a combination
thereof.
[0058] Sensor Calibration and Alignment
[0059] The sensor node(s) and module(s) should be properly aligned
with respect to features of the human body to enhance accuracy and
precision of the sensor readings. There may be alignment mark(s)
integrated on or in the garment(s), which helps the user align the
sensors properly. Alignment mark(s) can be placed on the sleeves of
the arms, chest panel, back panel, shoulder panel, leg panels, and
other places where it is important to indicate sensor position on
the human body. Alignment mark(s) and features are further
discussed below. Alignment instructions can also be visually
indicated within the application software or within a user manual
pamphlet that is included in the retail packaging. The position of
the sensor node(s) and sensor module(s)--which may be integrated
permanently into the base fabric of the garments--can be fixed
relative to the alignment mark(s) of the garment(s). The position
is set by angle and distance with respect to the alignment marks.
Additional calibration techniques exist to correct positioning
errors injected into the system during the alignment process
(further discussed below).
[0060] Optionally, the system may include ankle or wrist sensor
bands (to be worn like a typical watch or sports fitness band),
gloves, and/or compression socks to be worn snuggly to the skin so
as to limit relative motion between the device and the body. The
sensor node(s) and module(s) are preferably positioned where there
is a bony surface, as this impacts sensor accuracy. The motion
sensors should preferably not be placed on a muscle. It is more
effective to capture movement of the major bones of the human body
because analyzing kinematics of human motion generally involves
estimating the movement of the bone structure of the human body
that results from muscle activity (rather than motion of muscles
themselves). When the human muscle fibers contract (for example),
the skin surface migrates. The AMG record movement of the sensor
node(s), module(s), and/or band(s), and are agnostic to what causes
the motion. With a bony surface, there is less chance of skin
migration. It is noted that folding or rolling up a shirt sleeve or
pant leg may also compromise accuracy and is preferably avoided. It
is also noted that certain sensors may need to make contact with
the skin to function properly, such as EMG, ECG, pulse oximetry,
and HR sensors (further discussed below).
[0061] The garment(s) have a compression fit against the surface of
the human body (i.e., be form fitting), such that there is minimal
relative movement between the skin and the sensor nodes, the sensor
module, and/or sensors in the wrist or ankle bands. Preferably the
garment(s) provide a slight compression against the human body so
as to press the sensor node(s) and sensor module(s) against the
human body, reducing relative movement. The garments preferably
exhibit a compression fit on the torso region, arm region, hip
region, and leg region.
Garments should not be loose fitting or otherwise too large on the
user's body.
[0062] In one method for calibrating the sensors, the user may
assume a known pose and perform a quick predefined activity, such
as (for example) jumping jacks or a randomized motion identified by
the application software. This process can take under 10 seconds.
This process should be done before a workout session or athletic
activity is started, and should only be needed once after initially
putting on garment(s) and (if applicable) sensor bands to acclimate
the systems to the environment. Calibration is important because
the relative position of the sensors change each time a person puts
on the garment(s)/sensor band(s). Assuming a known pose aligns the
assumptions of the application software and the actual position of
the human body. One example of a known pose is the "T-pose" (see
FIG. 6). The application software is designed to make assumptions
on the position of the human body and it is the responsibility of
the user to replicate the known positions. In this manner, the
software can accurately extract position and align the movement of
the sensors to the estimation of movement within the software. A
known pose calibration helps correct/adjust for discrepancies
between the intended sensor mounting location and the actual
mounting location. This calibration can be done in any
environment.
[0063] The system may also employ random motion calibration by
requiring the user to do a series of jumping jacks or warm-up
exercises to properly calibrate the sensors. These random movements
and rotations may be used to acclimate the magnetometer component
of the AMG to the magnetic fields in the intended workout location.
This method can also be used to automatically identify the
locations of the sensor module(s) in the strap model
configuration(s).
[0064] Charging
[0065] A charging station can be provided for charging the sensor
module(s), nodes, and band(s), either directly or indirectly. The
batteries can be (for example) lithium polymer-ion batteries, or
any other chemistry that allows the battery to be rechargeable. The
charging station could also be used to charge wrist and ankle
band(s), if included. Charging might require from 15 minutes to 4
hours, and the user may have to charge the sensor after (for
example) 4 hours to 20 hours of continuous use. The charging
station may charge battery sizes from 50 mAh to 1000 mAh. The
electronic components may have low battery level indicators,
preferably as LEDs placed on the circuitry to indicate battery
charge level.
[0066] Feedback
[0067] The user receives a combination of real-time feedback and
post-athletic movement feedback. This is useful for new athletes as
well as veteran athletes. Regular and accurate feedback on
performance can help an athlete train more optimally, create
awareness to prevent injuries, recover from previous injuries, and
reach desired performance goals. Real-time feedback is provided
during an exercise or athletic movement. Post-exercise feedback is
provided after an exercise or athletic movement is completed, for
review during a rest period or whenever there is downtime. The user
interface can mimic actual feedback that a live personal trainer,
coach, therapist, or doctor would administer (if only to enhance
usability), but the system's feedback is generally more accurate
and possibly more conclusive and useful. The user can receive
feedback or information from the system through visual cues, audio,
and vibration in real-time or at a later time based on system
settings, or a combination thereof.
[0068] The computing device can display, for example, a virtual
representation of a human that demonstrates ideal movement,
technique, and/or posture for the user to emulate, as shown in FIG.
2. A graphical display like the one in FIG. 2 can be used to
showcase the optimal versus the actual movement of the user to
assist the user in making adjustments to reach optimal form and
technique. Methods of providing guidance and feedback on
corrections to be made to movements include, for example, color
coding of body segments or vector lines representing correct vs
incorrect form factor. In this manner, the user can manipulate his
or her body to achieve goals more quickly in an easy to understand
and effective way. Graphs and/or charts can be used to provide
indicators of user performance, such as (for example) set
completion, exercise completion, movement completion, range of
motion, power, eccentric and concentric phase, balance, heart rate,
caloric expenditure, tempo, acceleration, velocity, position,
gamification score, form efficiency, rest time, distance traveled,
force of impact, 3D avatar movement, progression of performance
over time, or a combination thereof.
[0069] Real-time feedback is processed and given by (for instance)
a wireless-enabled computing device, such as a smart phone, or a
stationary computing device, such as desktop personal computing
device. Examples of feedback provided are discussed below.
[0070] (1) Voice feedback: Using, for example, an integrated
speaker system, a headphone, or an external audio system that
connects with the computing device, the system can use a voice to
speak to the user. The code base for voice feedback can use (for
example) natural language processing (NLP). Many operating systems,
such as those offered by Android or Apple, have built in APIs
(application programming interfaces) that offer easily accessible
and free-to-use software components to integrate voice feedback
into the application software. Typically, this computation is
performed on-board the wireless-enabled computing device. The
computations may also take place on a server that is remote from
the mobile or static personal computing device(s). More details on
types of voice feedback metrics are provided below.
[0071] (2) Visual Feedback: (i) Charts and graphs can be a succinct
and effective way to provide feedback. While the user is exercising
or performing an athletic movement, it is important to provide
information that is not distracting and can be easily understood
within the time frame of a quick glance at the GUI. More details on
types of charts and graphs feedback metrics are provided below.
(ii) 3D avatar movements can provide, for example, replays of prior
movements, and can allow for the ability to pan and rotate around
the 3D avatar to further study movement. This can be a very
effective communication tool to easily communicate the quality of
movement and to provide guidance to improve movement. This can be
essential in helping a user prevent injuries and achieve their
performance goals by critiquing a user's performance and analyzing
weaknesses to iteratively improve upon the movement. This also
enables ghost movement comparison (i.e., comparing current movement
(for example) to past movement, or (as another example) to an
idealized version of the movement). The ability to compare movement
with a validated and optimal movement in a visual manner can be a
very effective communication tool to users. Often, a user's form
suffers during athletic movements or exercises. Having a simplistic
representation can provide a very effective and easy to understand
correction platform for a sufficiently complex problem. (iii) Light
feedback: the garment(s) preferably contain lights in the fabric or
sensor circuitry (sensor node(s), sensor module(s), and band(s))
where multi-color LEDs or multi-color electroluminescent lights or
fabric may help indicate performance metrics. Lights may be used
for injury prevention and general notifications and alerts about
performance or otherwise.
[0072] (3) Tactile feedback: Vibration motors affixed to the
garment(s), sensor node(s), sensor module(s), or band(s) may emit,
for example, a 0.4 to 800 Hz (frequency) vibration for 50 ms to 200
ms (time). Vibration may be used for injury prevention and general
notifications about performance or otherwise.
[0073] Post athletic feedback may be processed and given from a
wireless-enabled computing device, such as a smart phone, or from a
stationary platform, like a desktop computer. The code base for
visual feedback may be included on the wireless-enabled computing
device. Many operating systems, such as offered by Android or
Apple, have built in APIs that offer easily accessible and free to
use software components to integrate charting and graphic tools
into the application software. The computations may also take place
on a server that is remote from the mobile or static personal
computing device(s). More detailed graphs and charts can be used to
communicate progress over time. In some applications, detailed
progress is essential and in other areas, smaller amounts of
information are needed. Post-exercise feedback metrics are further
discussed below. Using 3D avatar movement, users can compare their
movements with a 3D representation within the GUI of the
application software. This visual representation may help the user
determine and identify areas for improvement. The 3D avatar can
help coach the user and it may bring an exciting user interface
experience to the user.
[0074] Usefulness of Feedback to the User
[0075] (1) Reaching Goals Faster and Safer: Feedback that a user
can easily understand and articulate allows the user to adjust
movements and change performance with relatively little downtime.
The system thus aims to provide information and exercise data that
are actionable and contextual to help the user achieve his or her
desired performance goals sooner, reduce injuries, etc. This type
of feedback can be easily custom tailored to any application where
movement form is the key to success.
[0076] (2) Calculations: These metrics and more could be calculated
on the sensor module(s), but preferably are calculated on-board the
wireless-enabled computing device, server, or stationary
application where there is possibly an abundance of computational
resources.
[0077] (3) Competition: The resulting metrics can provide an
appealing and competitive user experience. The user may, for
example, publish metrics on social media platforms, such as
Facebook, Twitter, Instagram, etc., to compete with friends and
family. The user can also compare results to others through the
software user interface itself.
[0078] (4) Data Aggregation and Reporting: The resulting metrics
can be aggregated into one user interface for a coach, trainer,
therapist, doctor, team, exercise class, or other groups of people
who train together, at the same venue, perform the same exercises,
or otherwise wish to view each other's metrics. This is useful (for
example) for a coach to be able to view metrics and progress for
multiple users at once in one software interface. This enables the
coach and the users to see each other's progress, compete with each
other, and compare performance to each other.
[0079] (5) Use Cases: The following metrics can be presented in
real-time (during the exercise or athletic movement) or post
exercise or athletic movement. They may be presented in various
formats, such as graphs and charts or via voice feedback. The list
of feedback mechanisms below is an example of how many metrics can
be calculated, but is not intended to limit the metrics as they may
vary depending on the athletic movement application.
[0080] (i) Repetition Counting
[0081] A significant pain point when exercising or performing
athletic movements (if applicable) is to keep track of repetitions
and sets completed. Values are often forgotten or not tracked
properly. The application software can keep track of repetitions,
as well as the magnitude of repetitions and sets, to provide
performance progress analysis and feedback. Without automatic
repetition counting, the user would have to enter each repetition,
and that can easily create an unappealing user experience and
potentially make it less likely a user will continue to work
towards his or her goals.
[0082] Repetitions can be calculated through, for example, machine
learning techniques, such as gathering many data samples and
analyzing the pattern and identifying trends in real-time. Other
methods include looking at the orientation and the sequence of
orientation of all or a specific set of (focused on a specific
exercise or athletic movement) limbs to set thresholds and identify
patterns that qualify as a completed repetition. Other methods
include, but are not limited to, looking at joint angle between
limbs and establishing thresholds and patterns that count as a
completed repetition, or identifying inflections in limb angle
rates. This is one approach, however, as repetition counting can be
determined using other methods.
[0083] (ii) Completion of Exercises, Sets, and Movements
[0084] By virtue of the repetition counting, orientation
recognition, and machine learning techniques, the application
software can recognize when the user has satisfactorily completed
the desired exercise, set, or movement. For example, in a bench
press, the application can recognize when the user has lowered the
bar all the way to his/her chest, and extended the arms fully
toward the sky to complete the movement. As another example, the
application can recognize whether the body angle has appropriately
entered an "inverted V" to be sufficient to be classified as a
"downward dog" in yoga.
[0085] (iii) Range of Motion (ROM)
[0086] ROM is related to injury prevention and maximizing benefits
of exercise routines. If range of motion begins to decrease as the
user completes repetitions and more sets, the user may be getting
fatigued, which in turn increases the likelihood of an injury. The
ROM metric is also important for enhancing the effectiveness of
exercise routines, and discouraging short cuts during exercises.
ROM can be calculated using an algebraic method, by looking at the
orientation of the limbs and using trigonometry and forward or
inverse kinematics to track a specific point on the body. By
analyzing the path and trajectory, it is possible to determine the
ROM. This is one approach, however, as ROM can be determined using
other methods.
[0087] (iv) Power
[0088] Power is related to the amount of effort that goes into each
repetition and set, and is a fundamental metric for certain
exercises and athletic movements. Power can be used (for instance)
to assess the likelihood of an injury as the power generated by a
user decreases with more repetitions. The pattern and trends of
power related to athletic movements can provide insights into the
human body. Power calculations generally include determining work
divided by time. Time can be tracked within the application
software with respect to orientation. Work can be determined by the
product of mass lifted (body weight of limbs added with weight of
resistance) and distance. Using the graphical user interface (GUI)
of the application software, the user can enter his or her weight
as well as the weight of the objects to be lifted. Distance can
also be calculated algebraically, in a similar fashion as for the
ROM.
[0089] (v) Eccentric Phase and Concentric Phase
[0090] These metrics are particularly relevant to rigid motions,
such as weight lifts. When the muscle lengthens, this is the
eccentric phase, and when a muscle shortens, this is the concentric
phase. For example, in a bench press, the downward motion is the
eccentric phase and the upward motion is the concentric phase.
Monitoring eccentric and concentric phases can be useful for
evaluating form efficiency and enhancing muscle development. These
can be determined by analyzing the displacement with respect to
time of certain body parts, and monitoring kinematics of specific
limbs. Typically, the time component is the duration of the
downward or upward stroke. This is one approach, however, as
eccentric phase and concentric phase can be determined using other
methods.
[0091] (vi) Balance
[0092] It is useful to determine whether the user is inadvertently
favoring a portion of the body, such as by identifying if a user is
exercising the left bicep more than his or her right side. This
metric is also useful in assessing stability with such exercises as
Yoga. This is one approach, however, as balance can be determined
using other methods.
[0093] (vii) Heart Rate
[0094] Heart rate is useful for maintaining health and measuring
performance in many applications. It can also be used as an input
to calculate caloric expenditure and overall performance.
[0095] (viii) Caloric Expenditure
[0096] Also referred to as calories burned, this metric is useful
in maintaining weight and tailoring diets, as well as for selecting
and changing workout regimen. Estimating caloric expenditure
involves such variables as user height, gender, age, and body
weight. Different methods for calculating caloric expenditure are
available, with varying levels of accuracy and precision. However,
based on movement and activity level, power calculations and basal
metabolic rate estimates are additional ways of determining caloric
expenditure.
[0097] (ix) Tempo (Frequency)
[0098] Consistency of movement is important for athletes as they
train to replicate movements with precision. Tempo, which is not
relevant to all movements, affects muscle groups differently. For
example, moving slowly can affect the slow twitch muscle fibers,
and fast movements can train and focus on fast twitch movements.
Tempo is thus an important metric for users wishing to focus on
training particular muscle fibers. Tempo can be calculated using,
for example, the frequency of repetitions over time and an analysis
of range of motion. This is one approach, however, as tempo can be
determined using other methods.
[0099] (x) Acceleration
[0100] Acceleration is important for users in many applications.
Acceleration is the change in velocity, so it measures how the user
is affecting the velocity at which the movement is being performed.
Changes in acceleration can be an indicator of better or worse
athletic performance, and can also be an indicator of likelihood of
injuries.
[0101] (xi) Velocity
[0102] Velocity is important for users in many applications.
Velocity is the change in position, so it measures how quickly the
user is performing the exercise. Changes in velocity can be an
indicator of better or worse athletic performance, and can also be
an indicator of likelihood of injuries. Examples where velocity is
of paramount importance include a baseball swing and a baseball
pitching motion.
[0103] (xii) Position
[0104] Position is important for users in many applications. In
many repetitive exercises and movements, the user should be in the
same position before, during, and after the exercise. Position
refers to the precise location in 3-dimensional space of every
joint on the body, and the relative positions between those joints.
The difference between being in the optimal and suboptimal position
can be the difference between a good and poor performance. Being in
the incorrect position can increase the likelihood of injuries.
Examples where position is of paramount importance are many
full-body weightlifting exercises, such as a squat, deadlift, and
power clean.
[0105] (xiii) Gamification Score
[0106] Video games can be entertaining, and the use of "scores" (a
unit-less measure) can be a motivator. This can be applied to
fitness to generate excitement and competitiveness. Users can
compare their game-like scores, which are representative of
performance and progress, with those of other users. Because such
metrics as power and total weight lifted are specific to an
individual user, they cannot readily be compared with (for
instance) the values for a user of another gender and age (because
the comparisons are so fundamentally different). However, using a
normalized score system based on select variables, such as gender,
body weight, etc., the exercises can be gamified with respect to
users in other demographic groups as well. This is one approach,
however, as gamification score can be determined using other
methods.
[0107] (xiv) Form Efficiency (FE)
[0108] Consistency can be important to a user's success as an
athlete. A FE metric can be based on, for example, orientations of
several limbs and specific trajectory of the human body. A user
(for instance) can improve on golf swings, baseball pitches,
basketball throws, weight lifting movements, etc., by enhancing
consistency. FE could be calculated by comparing a trajectory of
actual movement relative to ideal movement. Ideal movement can be
based upon, including, but not limited to, the user's body
geometry, age, gender, athletic movement, and more. FE units can be
in percentage, taken by analyzing the difference between the ideal
versus actual movement in a specified time. Many assumptions can be
taken to determine the difference in trajectories. The software
could identify the upper and lower bounds of the trajectory of
movement to identify levels of curve fit. From these levels of
curve fit, FE could be established based on the bounds established.
This is one approach, however, as FE can be determined using other
methods.
[0109] (xv) Rest Time
[0110] In some applications, users perform exercises and rest in
between sets of the exercise. One measure of the efficiency and
effectiveness of training is the rest time between sets. The
software interface can measure the time in between sets and report
back to the user. By integrating the measure of rest time with
other metrics and tracking progress over time, the software can
enable users to identify how adjusting their rest time could affect
performance.
[0111] (xvi) Distance Traveled
[0112] In some applications, users perform exercises where one of
the metrics is overall distance traveled. Examples include running,
cycling, and swimming. The software interface measures the distance
traveled and reports back to the user. By integrating the measure
of distance traveled with other metrics and tracking progress over
time, the software can enable users to identify how to improve
their distance traveled.
[0113] (xvii) Force of Impact
[0114] In some applications, the force of impact relates to the
likelihood of injuries. For example, in football and hockey, the
force and directionality of impact during collisions can relate to
the likelihood of concussions. As another example, in running and
jumping, the force and directionality of impact of the user's foot
striking the ground can relate to acute and chronic lower body
injuries. The software interface measures the force of impact in
three dimensions over time and reports back to the user. The
software interface can give real-time alerts to users, coaches,
trainers, therapists, and doctors on force of impact. Machine
learning algorithms can relate the force of impact to the
likelihood of injuries.
[0115] (xviii) 3D Avatar Movement
[0116] In helping users identify and improve quality of movement to
prevent injuries and maximize benefits of time spent, motion sensor
output is coupled with data fusion algorithms to feed real-time
form data to a processing unit for rendering and illustrating a
3-dimensional human avatar that mimics the movements of the user.
The ability to visualize performance adds depth to the fitness
experience, providing an insightful tool to help coach and guide a
user to improve performance. The 3D avatar can be used in at least
two ways: comparison and replay.
[0117] Replay refers to the ability to pan and rotate around a 3D
avatar to further study movement. It can be important to help users
identify areas of improvement and visualize how they can improve.
Panning and rotating presents new vantage points for the user.
Moreover, the ability to repeatedly replay movements helps the
process of analyzing, processing, and understanding the
information.
[0118] Regarding comparison, there are various ways to communicate
how to improve an athletic movement or provide guidance to move in
a different method. For instance, the 3D avatar can have a ghost 3D
avatar superimposed directly on top of the user's 3D avatar. The
superimposed avatar can represent, for example, a gold standard for
particular movements. Moreover, the superimposed 3D avatar can take
the form of a famous or recognizable figure, such as (for example)
a professional athlete, or their friend and family, for a more
exciting and appealing user experience. Explicitly showing the user
how to move or improve visually can make it easier for the user to
reach desired performance goals.
[0119] The 3D avatar can be implemented, in part, using open source
platforms--like Unity and Unreal game engines made by Epic Games,
and CryEngine made by Cryteck--which are traditionally used by game
developers. Such a component could provide the base calculations
for the 3D avatar and human skeleton. To activate and move the 3D
skeleton, the system can use various software components working in
harmony: a signal processing component (discussed later), a body
estimation component, and a body kinematics component.
[0120] The second component (i.e., the body estimation component)
is executed every time a new determination of body height or weight
is established. This component is executed early in the user
configuration process to learn about the user and generate data
used to calculate specific human body kinematic information and
feedback data. This component can be part of the source code as an
object for which data can be input, processed, and output for use
by other components.
[0121] For analysis of the kinematics of human motion, the system
can identify the human body having multiple segments, joined
together to form a system of chains and linkages. The segments can
include, for example, the forearm, upper arm, thigh, shank, foot,
head and neck, upper trunk, middle trunk, lower trunk, and fingers.
Based on a user's input of body height and weight, the segment
mass, moment of inertia, and center of mass of each linkage can be
estimated. The user may also input the lengths, circumferences,
masses, and/or other measurements of each segment of his/her body.
The length and cross section of each segment is estimated to render
segment mass. The joint degree of freedom can range from one to
three degrees of freedom, depending on the specific joint.
[0122] The user can also input into the application whether or not
specific parts of his/her body are sore, fatigued, strained, or
otherwise injured. The user can also input other information that
relates to his/her lifestyle, training, and exercise, such as:
information on sleep, information of food and water consumption,
mood, stress level, etc. The app can also integrate with other
applications to collect and share data that relates to the user's
lifestyle, training, and exercise.
[0123] The body estimation component may involve multiple different
types of components, modules, processes, systems, and the like. For
example, the training system may include one or more of: estimated
state component(s); update processing component(s); active input
elicitation component(s); short term memory component(s); long-term
memory component(s); math model component(s); reference data
component(s); cloud computation component(s); services
orchestration component(s); task flow models component(s); service
models component(s); and/or output processor component(s).
[0124] The body estimation component, which can be part of system
software, receives body geometry as an input from the user. Body
geometry information can be transmitted through the processing
circuitry and stored in a database. The user may subsequently be
allowed to alter the body geometry relating to the body height and
weight to modify the estimation of the body algorithm. In this
manner, the data processed from the body estimation component is
manipulated to be passed to other components, tasks, services, and
so on to unify the elements of the training system. The body
estimation component can allow other elements of the training
system to calculate and make recommendations to complete the method
to characterize, analyze, and supply feedback to a user relating to
human kinematics.
[0125] The third component (the body kinematics component) defines
the orientation and the position of human body segments and joints.
The body kinematics component identifies the user's actual movement
through bio-information collected from one or more personal sensing
device(s). Related kinematic data includes, but is not limited to,
range of motion, power output, optimal movement, repetition
counting, movement evaluation, and velocity. This component can
also be implemented as part of the system's software as an object
for which data can be input, processed, and output for use by other
components. The component calls processed data relating to the
human body geometry and personal sensing device physiological data.
This component is executed every time a user wishes to analyze a
body posture, medical related movement, or athletic movement. This
task may or may not be conducted simultaneously when a user is
performing exercise movement or posture.
[0126] The body kinematics component may include different types of
components, modules, processes, systems, and the like, that may be
implemented and/or instantiated using hardware and/or combinations
of hardware and software. For example, the training system may
include: one or more simultaneous active signal processing
component(s); estimated state component(s); comparison state
component(s); inverse kinematic model component(s); update
processing component(s); time step tracking component(s); active
input elicitation component(s); short term memory component(s);
long-term memory component(s); math model component(s); reference
data component(s); data compensation model component(s); Cloud
computation component(s); services orchestration component(s); task
flow models component(s); service models component(s); output end
effector position component(s); and/or output processor
component(s).
[0127] The body kinematics component work flow begins as the
personal sensing device begins to transmit signals. This component
can receive input data relating to segment movement measurements,
temperature measurements, ECG measurements, EMG measurements,
pulse-oximetry or a combination thereof. The rotation rate and
angular orientation information is calculated for each respective
body segment, for which processed data includes, but is not limited
to, segment orientation, relative segment position, joint position,
and relative joint position. The output data is directed to
kinematic analysis algorithms that use forward kinematics and joint
constraints to calculate translation position and rotation of the
body end-effector.
[0128] The body kinematics component may have the ability to
recognize movement automatically, as the component may contain
domains, tasks, and services which analyze movement data from one
or more personal sensing device(s). Based upon each
characterization of movement, the corresponding movement
corresponds to a designated kinematic movement path. In this
manner, the data processed from the body estimation component
renders an output to be passed to other components, tasks,
services, and so on to unify the elements of the training system.
The body estimation component can allow other elements of the
training system to calculate and make recommendations to complete
the method to characterize, analyze, and supply feedback to a user
relating to human kinematics.
Signal Processing
[0129] The signal processing algorithm preferably includes error
handling capabilities to compensate for external disturbances
injected into the system through dynamic motion events and/or
environmental effects.
[0130] (1) Dynamic Magnetic Disturbance Compensation
[0131] This method uses a recursive least squares fitting algorithm
to dynamically compensate for hard-iron and soft-iron disturbances.
The process uses (for example) an initial batch of magnetometer
readings (greater than nine samples) to define an ellipsoid.
Knowing a fixed magnitude vector should circumscribe a sphere under
random rotation, the parameters of the resultant ellipsoid not
representative of a sphere can be used to transform the raw
magnetometer readings into a fixed magnitude corrected frame. The
transformation properties are continually recalculated and updated
as new magnetometer samples are received to get its recursive
nature. The process is represented in FIG. 3 (related to magnetic
field disturbance compensation).
[0132] (2) Adaptive Measurement Error Matrix.
[0133] This method uses attributes of the sensor measurements to
adapt the elements of the measurement error covariance matrix of
the extended Kalman filter correction phase during runtime. This
modification directly influences the effectiveness of the
corrective phase of the Extended Kalman Filter. When in an
undesirable condition such that the accelerometer and/or
magnetometer are reporting values outside expected ranges, the
algorithm can discredit the contribution of the accelerometer
and/or magnetometer and rely more predominately on the gyroscope to
maintain attitude state estimation until the disturbance subsides.
The sensor attributes used in this method include, but are not
limited to, magnitude of accelerometer channels, magnitude of
magnetometer channels, and magnetic inclination angle derived from
the current state estimation of the extended Kalman filter and the
magnetometer channel measurements. The process is represented in
FIG. 4 (related to an adaptive extended Kalman filter process). The
method uses a continuous adaptive function, such as (for example)
an exponential decay function. Alternatively or additionally, the
method uses a piecewise function dependent on the sensor
measurement attributes and pre-defined thresholds. Coupled with
data fusion algorithms, the Kalman filters and the attitude heading
and reference system gather raw data from the AMG and process it
into movement orientation.
[0134] The firmware--which is generally a type of software that is
stored on read-only memory of (in this case) the System-on-Chip's
microprocessor (which is part of the electronic
circuitry)--provides the ability for the microprocessor to connect
and transmit information to and from several electrical components,
such as the sensors, buttons, etc. Algorithms, software tasks,
software services, and many more routines can be stored within the
firmware. There are a variety of languages that can make up the
firmware. For instance, C, C++, Java, and many other languages are
capable of expressing the algorithms, services, and tasks. The
sensor module(s) can connect directly and collect unprocessed data
from the sensor node(s) and (in some versions) the sensor band(s).
The following computations can include, but are not limited to,
signal processing, compensations, filtering, and error handling
capabilities.
[0135] A signal processing engine component removes sensor noise
and covariance from unprocessed data collected by various
physiological sensors within the personal sensing device circuitry.
The signal processing engine component characterizes, filters,
models, integrates, estimates, predicts, formats, reformats,
compensates variables with reference data, derives, and stores data
in long and short term memory from one or more personal sensing
devices. The component and all combinations of tasks and services
can be part of the software infrastructure as one or more object(s)
for which data can be input, processed, and output for use by other
algorithms. An exemplary flow chart can be seen in FIG. 5.
[0136] The signal processing engine component may include different
types of components, devices, modules, processes, systems, and the
like, which, for example, may be implemented and/or instantiated
via the use of hardware and/or combinations of hardware and
software. For example, the training system may include one or more
of the following types of systems, components, devices, processes,
and the like (or combinations of thereof): one or more simultaneous
active signal processing component(s); estimated state
component(s); variance or uncertainty of the estimate component(s);
update processing component(s); time step tracking component(s);
active input elicitation component(s); short term memory
component(s); long-term memory component(s); math model
component(s); reference data component(s); data compensation model
component(s); services orchestration component(s); task flow models
component(s); service models component(s); Cloud computation
component(s); output processor component(s).
[0137] The signal processing engine component may use services to
collect data in real time or in a calculated delayed time step from
one or more personal sensing device(s). In variations of the
system, the signal processing engine component can use orientation
data to represent absolute orientation of a user's kinematic
movement. This engine component can eliminate or reduce noise and
reduce the effects of sensor drift. It can also interact with
sensors including, but not limited to, one or more
accelerometer(s), gyroscope(s), magnetometer(s), temperature
sensor(s), electromyography sensor(s), electrocardiogram(s), pulse
oximetry(s), or a combination of thereof.
[0138] The signal processing engine component can be designed to
have a recursive nature, allowing real-time analysis or a
calculated delayed time step of one or up to several personal
sensing device(s) using several different types of components,
devices, modules, processes, systems, and the like, or a
combination of thereof, to produce statistically optimal estimates,
a combination of multiple-stage processes to eliminate (for
example) uncertainty in measurement, random noise, and covariance
data for an outcome of minimized covariance, error, and randomized
noise.
[0139] In this manner, the data processed from one or more personal
sensing devices can be manipulated to minimize covariance and error
for high accuracy and precision of calculated metrics, implemented
tasks, and services for a combination of different types of
components, devices, modules, processes, systems, and the like. The
data processing engine component unifies elements of various
components in the training system.
Garment
[0140] The garment is an interaction platform for the user. The
garment guides proper alignment of the sensor node(s) and the
sensor module(s) on the user's body. Alignment marks or features
placed on the garment(s) can be, for example, visual indicators
that are screen printed, sewn, embroidered, or otherwise. These
indicators could point to a specific orientation that the sensors
should abide by. The seams could be used as alignment marks; they
could indicate to the user (for example) that the sensors should be
facing in a certain direction and at a given angle to be in line
with a limb when the user is in a T pose (FIG. 6). There could also
be cutouts or apertures for the thumb or elbows or other limbs to
guide the user to wear the garment(s) properly. The user would
place their limbs through the cutouts for proper alignment of the
garments. These cutouts or voids would be specifically aligned with
the sensor node(s) or the module(s), so that when the user adjusts
the clothing, the sensor node(s) or the module(s) are dragged along
to land in the correct position. Sensor voids can be slots, holes,
or a variant of the aforementioned shapes which is placed on the
base fabric of the garment. It may occupy a cross sectional area of
up to 490 squared millimeters. The sewing pattern and geometry can
be similar to that used for buttons (FIG. 7).
[0141] All sensor nodes, electrically conductive fabric, and sensor
modules can be housed by or otherwise secured to the garment. The
sensor node(s) may be permanently integrated, woven, or adhered to
the garment(s). The sensor module(s) may be permanently integrated,
woven, or adhered to the garment(s), or may be removable. The
electrically conductive fabric is permanently integrated into the
garment(s). To enhance comfort (which can enhance usability as well
as effectiveness), the garment may be provided with sweat wicking
properties, anti-microbial properties, and be designed to increase
blood circulation, prevent injuries, etc. The garment may have
sensor voids placed on the surface of the base fabric for the
sensor node casing membrane to pass through for attachment to the
garment, electrically conductive fabric, and other components that
make up the electrically conductive fabric.
[0142] The garments can (for example) include: a long sleeve shirt
and pants; a short sleeve shirt and pants, along left and right
wrist sensors/bands; a short sleeve shirt and shorts, along with
left and right ankle sensors; a short sleeve shirt and shorts,
along with left and right ankle sensors as well as left and right
wrist sensors. Additionally, the system can affix sensors to other
body parts of interest including head, neck, lower-back, etc. The
system can also include one or more sensors secured to weight
lifting equipment, a ball appropriate for a sport, or an external
object that is appropriate to track orientation, position, or
distance.
[0143] Regarding materials, the garment can be made from a highly
elastic, breathable, lightweight synthetic blend of materials, such
as nylon, Lycra, polyester, natural fibers, a suitable combination
of natural and synthetic fibers, or spandex, to provide a
compression fit against the human body to properly attach the
sensors and gather movement data of the user. The garment provides
compression fitting to promote minimal relative movement between
the motion sensors and the user's body. The garment could be made
in varying sizes to accommodate any user, from children's sizes to
adult males and females. The garment could use standard practices
and techniques commonly used to construct compression fit clothing.
The garments could be available in a myriad of colors.
Sensor Nodes
[0144] The sensor nodes are designed to allow the garment(s) to be
capable of withstanding multiple machine washing and drying cycles
and harsh conditions during exercising, while collecting accurate
sensor measurements in a cost effective way. There are two primary
components that make the sensor node: the case membrane and the
electronic circuitry. The case membrane houses and protects the
electronic circuitry and provides physical alignment features to
hold the sensitive electronic circuitry in place to make it
possible for the garment(s) to record accurate and precise
measurements. It also interacts with the electrically conductive
fabric in a unique and robust manner to enable the system to
withstand machine washing and drying cycles. The electronic
circuitry itself can use industry standard manufacturing techniques
for sensory and electronic components that are packaged to fit
within the case membrane.
[0145] There may preferably be four sensor nodes permanently
attached on each garment (for example a shirt or pants) that can be
directly connected to one sensor module via electrically conductive
fabric. In such a version, two sensor nodes can be connected in
series, and another pair of sensor nodes can be connected in
series. Together, each pair of sensor nodes could be connected in
parallel to the sensor module. A sensor node is placed on: left and
right wrists or left and right mid-forearm; left and right upper
arms; torso; hip; left and right thighs; left and right shins; left
and right hands (which could include all fingers and palms); left
and right feet; and head.
[0146] Case Membrane
[0147] The sensor node includes a three-piece case membrane (FIG.
8):
[0148] Part A: This part is permanently attached onto the garment
in the final stages of assembly. The purpose is to secure the
assembled Part B and C (discussed below) onto the garment and to
provide alignment and protection for the electrically conductive
fabric, the pin(s), and the garment.
[0149] Part B: This part is permanently attached onto the garment
in the final stages of assembly. This part is designed to hold the
pin(s) and provide alignment geometry to hold the electronic
circuitry. Part B interacts directly with the garment and the
conductive fabric. It features one or more pin(s) which directly
penetrate and interact with the conductive fabric (FIG. 35).
[0150] Part C: This part features a void complete with alignment
features to hold the electronic circuitry. Part C is adhered to
Part B once the electronic circuitry is mounted into Part C. It
provides protection to the circuitry against impact forces such as
drops of more than 90 g. It also provides protection to the
circuitry and all other supporting electrical components against
dirt, debris, and liquids by a hermetical seal of the case
membrane. The protective casing membrane of the sensor node is
designed to prevent ingress of foreign liquid beyond one meter, and
achieve an ingress protection rating of up to IP68.
[0151] The case membrane, when assembled, creates a void for
placement of the electronic circuitry. The void is complete with
alignment lips and walls to properly align the electronic circuitry
into the void. In this manner, proper alignment between the pin(s),
the case membrane, and the electrically conductive fabric is
possible. The use of alignment lips, holes, walls, and other
features provides a mechanism to properly install and assembly the
sensor node(s) with the garment(s). The case membrane provides an
electrical connection between the electronic circuitry and the
electrically conductive fabric, separating each electrical channel
from short circuiting, and incorporating alignment tabs and lips to
ensure correct alignment of the electrically conductive fabric,
pin(s), and the electronic circuitry. The case membrane provides a
structural connection between the garment, conductive fabric, and
circuitry. The case membrane also isolates the electrical circuit
from the user; it provides protection against electrically shocking
the user.
[0152] It is preferable that the sensor node case membrane is made
from a resilient, tough, and rigid plastic material. It is
important to select a material that is compatible with the
manufacturing process and one that provides adequate structural
rigidity for impact protection, and the ability to create a strong
hermetic seal for the circuitry and one or more pin(s) (further
discussed below). Some materials may include: Acrylonitrile
butadiene styrene (ABS); polycarbonate (PC); ABS and PC mixture;
poly(vinyl chloride) (PVC); polyethylenimine (PEI);
polyethersulfone (PES); poly(methyl methacrylate) (PMMA); and other
suitable materials. Possible manufacturing methods include plastic
injection molding, vacuum forming, machining, blow forming,
additive printing, and others. The case membrane's finish could be
left raw, or it could be painted, clear coated, etc.
[0153] Regarding sizing for the case membrane, and specifically
volume envelope, typical volumes may range from 352 cubic
millimeters and up to 110,000 cubic millimeters, as deemed
suitable. Smaller volumes may promote better ergonomics and comfort
at the expense of circuitry performance as it relates to wireless
signal performance. Typical wall thicknesses may range from 0.89 mm
to 5 mm. Thinner walls result in a smaller size, but at the expense
of structural rigidity, which may lead in an inability to create a
hermetic seal or may provide poor impact protection for the
circuitry. Thicker walls could have adverse effects on comfort for
the user wearing the garment because the case membrane could be
invasive.
[0154] Node Pin(s)
[0155] The case membrane includes one or more, preferably four,
pins, which provide electrical connection between the electronic
circuitry and the electrically conductive fabric. These pin(s) also
may provide a structural seal against the plastic material. The
pin(s), which may be glued, over molded, or compression fitted to
the case membrane, is not the same material as the case membrane.
These pin(s) can provide a structural seal against the plastic
material. The case membrane may provide a structural foundation for
one or more pins.
[0156] During extreme athletic motion, washing or drying cycles, or
other similar load conditions that place significant compression
and tension on the electrically conductive fabric, the garment(s),
or the case membrane, the pin(s) may be subjected to a radial force
which may create a bending force or other similar types of forces.
The structural connection between the pin(s) and the case membrane
withstand shear or yield in any fashion from more than 20 pounds of
external force.
[0157] The pin(s) acts as an electrical conduit by creating an
electrical connection between the conductive fabric and the
circuitry. The pin(s) is designed to penetrate the conductive
fabric in areas where there are conductive fiber(s) or materials.
This penetrate method creates a void through the fabric, and the
conductive fiber(s) or materials "hug" the pin(s), creating a good
electrical connection (FIG. 35). In a separate version, the pin(s)
"hug" the conductive fiber(s) or materials, creating a good
electrical connection. The opposite end of the pin is pressed
against the circuitry on specific conductive pads to establish an
electrical connection between the pin(s) and the electronic
circuitry. The pin(s) transmits, for example, from 300 micro amps
to 10 milliamps. The pin(s) facilitates connection to power,
ground, and one or more signal channels.
[0158] The pin(s) are preferably made from highly electrically
conductive materials, such as (for example) brass, copper, or
silver. Regarding volume envelope, typical volumes can range from
12.5 cubic millimeters to 400 cubic millimeters. The material of
the pin(s) should be selected to provide adequate structural
rigidity and to withstand any cantilever forces that are placed on
the pin(s) as the electrically conductive fabric migrates during
exercise. It is also important to select a geometry that creates a
connection with the conductive fabric. Possible manufacturing
methods for the pin(s) include lathe, screw machine, investment
casting, sand casting, etc. The pin(s) finish could be left raw, or
could have conductive paste coatings or other suitable coatings
that do not interfere with functionality.
[0159] Electronic Circuitry
[0160] The sensory technology used to gather measurements are
typically small electrical components, such as Integrated Circuits
(IC), that are typically Surface-Mount Technology (SMT) style
components that are attached on a Printed Circuit Board (PCB).
Different types of sensors that gather different types of
measurements--such as heart rate, blood oxygen, temperature,
pressure, altitude, acceleration rotation rate, magnetic flux,
etc.--can be fitted within the case membrane of the sensor node. In
particular, it is preferable to equip the electronic circuitry with
motion sensors--such as 3-axis accelerometers, 3-axis gyroscopes,
and 3-axis magnetometers (AMG)--coupled with the necessary IC and
System-on-Chip (SoC) to develop a system for analyzing the
kinematics of human motion (FIG. 9). In other versions, multiple
different measurements, such as heart rate, blood oxygen, skin
temperature, etc., could be coupled with a 9-axis AMG system for
more data visualization and various specialized applications (FIG.
16).
[0161] The motion sensors measure relative movement against an
inertial world reference frame. The case membrane provides
alignment of the motion sensors to ensure it is possible to align
the sensor with the human body to measure accurate kinematics of
human motion.
[0162] Electrical components can include, but are not limited to, a
combination of (FIG. 9 through FIG. 18):
[0163] (1) PCB: Used as a mounting platform for all the SMT style
components;
[0164] (2) SMT style components (FIG. 9 through FIG. 18): [0165]
(i) 3 axis-accelerometer for measuring acceleration; [0166] (ii) 3
axis-gyroscope for measuring rotation and rotation rate; [0167]
(iii) 3 axis-magnetometer for measuring local magnetic field;
[0168] (3) One or more microprocessor(s) (FIGS. 11, 12, 13, 16,
17): Used for computation(s) and for connection and operation of
the SMT components;
[0169] (4) Physiological sensors (FIGS. 16, 17, 18): these sensors
measure an organism's normal functioning, in this case related to
vitals such as body temperature, heart rate, blood oxygen, muscle
activity, sweat constituents, etc.; [0170] (i) may include optical
heart rate monitor or electrocardiogram, which could be helpful in
determining maximum heart rate with respect to athletic movements
or exercise; [0171] (ii) may include muscle impulses
(electromyography), which could be helpful in determining muscle
activity and the impact of movements on specific muscle groups;
[0172] (iii) may include skin temperature sensor, which could be
helpful for knowing the skin temperature to derive core
temperature, and for helping improve the accuracy of caloric
expenditure and other metrics;
[0173] (5) Temperature sensor (FIG. 9 through FIG. 18): there may
be a sensor that measures temperature of the circuitry for
temperature compensation; this temperature sensor could take
measurements of a specific location on the PCB;
[0174] (6) User interface components (FIGS. 10, 12, 13, 14, 15, 16,
17, 18): [0175] (i) Receiving component: Used for turning on power
to the circuit or providing general inputs into the system; can be
a toggle switch, button, knob, control, etc.; [0176] (ii)
Light-emitting diodes (LEDs): Could be helpful to provide
notifications; [0177] (iii) Vibration motors: Could be used to
provide general notifications.
[0178] (7) RF components: there may be components that transmit and
receive information over wireless channels.
[0179] The electrical circuitry may include one or more electrical
connection channels, including, but not limited to power, ground,
and signal. Preferably, the circuitry is constructed using typical
and industry-standard circuitry materials for PCB manufacturing,
such as solder, copper, silicone, Mylar, and more. Typical size
requirements range from 352 cubic millimeters and up to 110000
cubic millimeters. Smaller volumes promote better ergonomics and
comfort at the expense of circuitry performance as it relates to
wireless signal performance. The electronic circuit boards could
have a PCB manufactured using typical industry standards and
processes, and assembly could be accomplished using industry
standards and processes for SMT-style components.
Sensor Bands
[0180] Some users may not like to wear long sleeve shirts or pants
and would rather wear short sleeve shirts and shorts. As such, the
sensor nodes(s), sensor module(s), and electrically conductive
fabric may not reach the wrists or ankles to measure motion. To
address this issue, left and right wrist bands and ankle bands may
be used to acquire full body motion data. Some users may not like
to wear garments and prefer to interact with a system of one or
more sensors bands. Other locations include the wrists, upper arms,
torso, hip, thighs, shins, hands (including are all fingers and
palms), feet, and head. If desired, a left or right sensor band(s)
may be substituted with a smart wrist device or a device equivalent
to a smart watch or fitness band, such as a Fitbit HR or Apple
Watch, to analyze motion, if the substituting device has an
accelerometer, magnetometer, and/or a gyroscope with a compatible
wireless internet protocol such that it can synchronize and connect
with the system to transmit data and provide feedback. The system
may transmit data to and from the third-party device to use any
unique sensors it may have to aid in providing data visualization
and help in guiding the user on achieving performance goals. The
sensor band(s) can communicate to the wireless-enabled computing
device through any communications protocol deemed suitable. The
sensor bands may also first connect directly to a remote server,
then to the mobile or stationary computing device through (for
example) the internet. The sensor bands could also connect
wirelessly to the sensor module. The electronic circuitry can share
the same types of configurations as the sensor module; however, it
would not interact with the electrically conductive fabric.
[0181] The sensor band(s) preferably have a compact and
non-invasive enclosure, attaching securely to the surface of the
human body. The enclosure protects the electronic circuitry from
impacts and foreign contaminants such as water, debris, dirt, or
anything else that could interfere with circuitry operation. The
hard or soft enclosure (FIG. 19) produced from modern manufacturing
techniques and materials enclose the personal sensing device(s)
circuitry to provide modular functionality relating to installation
and removal, or can be directly integrated into several user
mounting mechanisms including, but not limited to, (for example)
shirts, shorts, tee-shirts pants, gloves, hats, helmets, flexible
membrane strap, or a combination thereof. The material preferably
would not have any adverse effects causing skin irritation, and
would provide good ergonomics for maximum comfort. The enclosures
of the sensor bands can use the same manufacturing methods
identified in the sensor node section. The enclosure of the sensor
band could use the same manufacturing methods identified in the
sensor node section.
[0182] A flexible membrane strap (FIG. 20) conforms tightly to the
exterior geometry of the personal sensing device (FIG. 19). The
sensor enclosure can be incorporated into the flexible membrane
strap or can incorporate a removable or attachable separate soft or
hard enclosure, which can be constrained in a variety of ways
including, but not limited to, interference fit, preload by
fasteners, or a locking mechanism. The tight conformability allows
the user to interface with features, such as buttons, switches, and
accessory ports, while protecting from dirt, debris, water, and
impulsive force. Alternatively, it may provide openings for
features, such as buttons, switches, and accessory ports for
ergonomic purposes. The flexible membrane strap provides openings
or a thin wall thickness for which light may permeate with adequate
visibility for the user to access ports such as a visual display
that includes, for example, LED, AMOLED, PMOLED, E-INK, and
switches.
Sensor Module
[0183] The sensor module, which is configured to allow the garment
to withstand multiple machine washing and drying cycles and harsh
conditions while exercising, collects accurate sensor measurements
from sensor nodes. There are three primary components that make the
sensor module: module case membrane, module electronic circuitry,
and the module firmware.
[0184] Case Membrane
[0185] The case membrane houses and protects the electronic
circuitry and provides physical alignment features to hold the
sensitive electronic circuitry in place (with respect to the human
body) to make it possible for the garment(s) to record accurate and
precise measurements. It also interacts with the electrically
conductive fabric in a very unique and robust manner to enable the
system to withstand a plurality of machine washing and drying
cycles. As discussed above, the sensor module can be connected with
each of two pairs of sensor nodes, each pair of sensor nodes having
two sensor nodes connected with each other in series (FIG. 21). A
sensor module may be placed on, for example, wrists, upper arms,
torso, hip, thigs, shins, or feet. The sensor module(s) are
preferably removable and re-attachable. The sensor module(s) can be
(for example) clipped, latched, or fastened into a holster that is
permanently integrated into the garment(s). Easy removal of the
sensor module can make maintenance (repair) and battery charging
easier.
[0186] Electronic Circuitry
[0187] As with the sensor nodes and bands, the electronics use
industry standard manufacturing techniques, having a form factor
and packaging that is unique and designed specifically to fit
within the case membrane. The sensory technology used to gather
measurements are typically small electrical components, such as
Integrated Circuits (IC), that are typically Surface-Mount
Technology (SMT) style components that are attached on a Printed
Circuit Board (PCB). The sensor module preferably includes wireless
transmission circuitry, such as low-energy Bluetooth to keep energy
consumption low and to help maintain small component size. Other
protocols, such as UWB, Wireless Fidelity (WiFi), Wi Max, Edge,
CDMA, Global System for Mobile Communications (GSM), WCDMA,
Metropolitan Area Network (MAN), Wide Area Network (WAN), Personal
Communication Services (PCS), General Packet Radio Service (GPRS),
Advanced Mobile Phone System (AMPS), 4G, 5G, and other variants of
802.x standards, and varying ranges, throughputs, and frequencies
can be used depending on the specific usage application. The sensor
module may also connect directly to a remote server through the
aforementioned internet networks to send data to be processed, then
transmitted to the GUI of a computing device to provide feedback.
The sensor module may interact wirelessly with the sensor node(s)
and the sensor band(s): the sensor node(s) and sensor band(s) may
wirelessly connect to transmit unprocessed data to and from sensor
module. The sensor modules can incorporate firmware using coding
languages as discussed above with respect to the sensor nodes. The
sensor module(s) can connect directly with, and collect unprocessed
data from, the sensor node(s) and the sensor band(s), if
applicable. Computations can occur on the on-board processing
circuitry and include, for example: signal processing,
compensations, filtering, and error handling, analogous to the
above discussion.
[0188] Module Case Membrane
[0189] The sensor module can include a four-piece case membrane
(FIG. 22):
[0190] Part Q: The holster. The holster is permanently attached
onto the garment with a retaining mechanism involving a latching,
clipping, fastening, or a combination thereof to capture Assembled
Parts L, M, N, O, and P in an easy to remove and install manner.
The holster interacts directly with the garment and the conductive
fabric in the same way as the sensor node. It features one or more
pins which directly penetrate and interact with the conductive
fabric (FIG. 35). The holster uses those same pin(s) to make
contact with the Assembled Parts L, N, and O.
[0191] Assembled Parts L and N: These parts house all electronic
circuitry and provide pin(s) which interact with the pin(s) that
are bonded within the holster. These parts can have feature(s)
which can interact with Part Q to accommodate a retaining mechanism
involving latching, clipping, fastening, or a combination
thereof.
[0192] Part K: The Cover. The cover is permanently attached onto
the garment in the final stages of assembly. Its primary purpose is
to secure Part Q onto the garment and to provide alignment and
protection for the electrically conductive fabric, the pin(s), and
the garment. The sensor module may incorporate one or more of Part
K to help stabilize and provide greater contact area between the
garment and Part Q for a more durable connection. It provides
protection to the circuitry against impact forces, such as drops of
more than 90 g. It provides protection for the circuitry and all
other supporting electrical components against dirt, debris, and
liquids by a hermetical seal of the case membrane. The protective
casing membrane of the sensor module is intended to prevent ingress
of foreign liquid beyond one meter and achieve an ingress
protection rating of up to IP68.
[0193] Parts N and L: The assembled case membrane includes a void
for the electronic circuitry. The void can include alignment lips
and walls to properly align the electronic circuitry into the void.
In this manner, proper alignment between the pin(s), the case
membrane, and the conductive fabric is ensured.
[0194] The module case membrane provides electrical connection
between the circuitry and the electrically conductive fabric. It
separates each electrical channel from short circuiting with
another, and it provides a structural connection between the
garment(s), electrically conductive fabric, and electronic
circuitry. The module case membrane isolates the electrical circuit
from the user; it provides protection against electrical shocks to
the user.
[0195] Module Pin(s)
[0196] The pin(s), which may be glued, over molded, or compression
fitted to the case membrane, is not the same material as the case
membrane. These pin(s) can provide a structural seal against the
plastic material. The case membrane may provide a structural
foundation for one or more pins. During extreme athletic motion,
washing or drying cycles, or other similar load conditions where
there is compression and tension placed on the electrically
conductive fabric, the garment(s), or the case membrane, the pin(s)
may be subjected to a radial force which may create a bending force
or other similar types of forces. The structural connection between
the pin(s) and the case membrane should not shear or yield in any
fashion from (for example) 20 or more pounds of external force.
[0197] The pin(s) acts as an electrical conduit by creating an
electrical connection between the conductive fabric and the
circuitry. The pin(s) penetrates the conductive fabric in areas
where there are conductive fiber(s) or materials (FIG. 35). This
penetration method creates a void through the fabric and the
conductive fiber(s) or materials "hug" the pin(s), creating a good
electrical connection. In alternative versions, the pin(s) "hug"
the electrically conductive fiber(s) or materials. The opposite end
of the pin(s) is pressed against the circuitry on specific solder
pads to establish an electrical connection between the pin(s) and
the electronic circuitry. The pin(s) is designed to transmit from
300 microamps to 10 milliamps. The pin(s) facilitates connection to
power, ground, and one or more signal channels
[0198] Gasket
[0199] Because the pin(s) can be used to provide an electrical
connection between the holster and the sensor module, the pin(s)
may be exposed to contaminants when the user is wearing the
garment(s) and performing exercises or otherwise making athletic
movements. Since the system may be subjected to a variety of harsh
environments, such as extreme heat, swimming pools (and the
chemicals therein), extreme cold, etc., it is beneficial to protect
the pin(s) against foreign contaminants, such as liquid, dirt,
debris, sweat, etc. A gasket or other type of compression device
that forms a closed ring about the pin(s) is used to provide
protection and create a hermetic seal for the pin(s). The gasket
seal can be effective when the sensor module is installed on the
holster, and is properly attached and aligned in such a way that
the compression device creates high localized stresses, which do
not yield the material, but provide adequate sealing pressure. The
gasket is made from a pliable material to be compressed between the
holster and the sensor module.
[0200] Materials
[0201] Case Membrane--Part L, N, Q, and K: It is preferable that
the sensor module case membrane is made from a resilient, tough,
and rigid plastic material. It is important to select a material
that is compatible with the manufacturing process and one that
provides adequate structural rigidity for impact protection, shear,
torsional loads, etc., while providing the ability to create a
strong hermetic seal for the electronic circuitry and one or more
pin(s). Some materials include: ABS, polycarbonate, an ABS and PC
mixture, PVC, PEI, PES, PMMA, and materials deemed suitable.
[0202] The case membrane includes one or more pins, preferably
eight pins, which provide electrical connection between the
electronic circuitry and the electrically conductive fabric.
Preferably, the pin(s) are made from highly electrically conductive
materials, such as brass, copper, or silver.
[0203] The gasket preferably is made using materials with pliable
characteristics, and that are resilient to detergents and solvents,
such as laundry detergent, water, oil, and debris. The durometer of
such gaskets can range from 10 A to 70 A Shore. Suitable materials
may be silicone, polytetrafluoroethylene (PTFE), neoprene, Buna-N
(nitrile rubber), etc.
[0204] Size
[0205] Volume envelope for the module case membrane (parts L, N, Q,
and K) can range from 352 cubic millimeters up to 110000 cubic
millimeters. Smaller volumes promote better ergonomics and comfort
at the expense of circuitry performance as it relates to wireless
signal performance. Typical wall thicknesses may range from 0.89 mm
to 5 mm. Thinner walls result in a smaller size, but at the expense
of structural rigidity, which may lead in an inability to create a
hermetic seal or poor impact protection for the circuitry.
[0206] Volume envelope for the pin(s) can range from 12.5 cubic
millimeters up to 400 cubic millimeters. The pin(s) should have a
geometry that provides enough structural rigidity to withstand
cantilever forces that are placed on the pin(s) as the conductive
fabric may migrate during exercise. It is also important to select
a geometry that creates a connection with the conductive
fabric.
[0207] Manufacturing Methods and Finishes
[0208] Possible methods for manufacturing the module case membrane
and gasket (Parts L, N, Q, K, and P) are plastic injection molding,
vacuum forming, machining, blow forming, etc. Potential methods for
manufacturing the pin(s) include lathe, screw machine, investment
casting, sand casting, and more. The module case membrane can be
finished as the sensor case membranes.
[0209] Electronic Circuitry
[0210] As with the sensor nodes, the electrical components can
include, for example, a PCB used as a mounting platform for all the
SMT style components (such as accelerometers, gyroscopes, and
magnetometers. The sensor module can include user interface with a
receiving component for inputs into the system, and LEDs and
vibration motors for visual and tactile cues, instructions,
notifications, and warnings. The wireless transmission circuitry
can include the various wireless protocols requiring an antenna,
but the antenna geometry will vary for different protocols, such as
UWB, Bluetooth, Wireless Fidelity (WiFi), Wi Max, Edge, CDMA,
Global System for Mobile Communications (GSM), WCDMA, Metropolitan
Area Network (MAN), Wide Area Network (WAN), Personal Communication
Services (PCS), General Packet Radio Service (GPRS), Advanced
Mobile Phone System (AMPS), 4G, 5G, and variants of 802.x
standards, and varying ranges, throughputs, and frequencies. Near
field communication technology may be incorporated as well.
[0211] A system on chip (SoC), which is a packaged integrated
circuit (IC) that integrates all components of an electronic
system, such as (for example) Bluetooth, WiFi, processing, etc.,
can be used. Typically, the wireless transmission circuitry is
packaged within a SoC. One or more radios for receiving and or
transmitting of data for one or more wireless transmission links
can be included (FIGS. 24 to 30). The radios may serve as remote
station for other devices, such as the sensor node(s) and sensor
band(s) connecting wirelessly with the sensor module, which could
then communicate with the wireless-enabled computing device. Direct
connections using conductive materials, such as (for example)
electrically conductive fabric or wires, would not be
necessary.
[0212] The sensor module includes a battery (FIGS. 24 to 30), such
as a lithium polymer-ion or any other modern chemistry suitable for
consumer grade products. The lithium polymer battery is not
removable, so as to maintain seals and otherwise avoid compromising
integrity; if the battery is removed, the entire sensor module unit
would likely need to be replaced. Inductive coupling components
could be beneficial to provide wireless charging as well as
contactless connections between the holster and the brain case
membrane. This way, there would be a lower likelihood of the module
case membrane breaking, and fewer opportunities for a leak path to
originate because there are fewer apertures present.
[0213] Microprocessor(s) for computations and for connecting and
operating SMT components, a SoC with wireless transmission
circuitry packaged therein, and physiological sensors could be used
as with the sensor nodes (FIGS. 24 to 30). Random Access Memory
(RAM) would be used to store processed or unprocessed data from
sensor node(s) and band(s) for times when the sensor module is not
able to exchange data with the wireless-enabled computing device
(and/or the server) (FIGS. 27 to 30). For example, if a user is
swimming while wearing a smart garment, and water is
absorbing/dampening signals (and preventing transmission of
wireless data), the RAM could be used to temporarily store data
until the data can be transmitted.
[0214] The electrical circuitry may have one or more electrical
connection channels, including, but not limited to, power, ground,
and signal. The sensor module may communicate with the sensor nodes
through protocols including, but not limited to, Inter IC bus
(I2C), synchronous serial interface (SSI), serial peripheral
interface (SPI), universal asynchronous receiver/transmitter
(UART), pulse-width modulation (PWM), pulse-code modulation (PCM),
and other types of serial communications protocols.
[0215] Typical size requirements can range from 22,600 cubic
millimeters up to 90,500 cubic millimeters. Smaller volumes promote
better ergonomics and comfort at the expense of circuitry
performance as it relates to wireless signal performance. The
circuitry may be constructed using standard materials for PCB
manufacturing, such as solder, copper, silicone, etc. A PCB may be
manufactured first, before SMT-style components are assembled using
relevant industry standards and processes.
Electrically Conductive Fabric
[0216] The primary role of the conductive fabric is to transmit
data and power throughout a garment in a comfortable, aesthetically
appealing, safe, and cost effective manner. Several devices can be
connected together to transmit power and ground signals and one or
more data transmissions, in a manner that has low electronic signal
attenuation. If there is high signal attenuation, it will affect
the data rate transmission between the sensor nodes and the sensor
module. The goal is to have fast data rates to minimize on
measurement latency. Ideal electrical characteristics could
resemble those of a typical 28-30 gauge copper wire.
[0217] The electrically conductive fabric is designed to be
stretchable, thin and light weight; the electronically conductive
fabric is integrated directly into the garments without intruding
on the function of the garment as sports outerwear. The
electrically conductive fabric is designed to provide a water proof
connection between the sensor node(s) and sensor module(s). The
electrically conductive fabric is fully submerged in a textile
coating to isolate the user from electrical shock, which would
cause discomfort to the user, although the power levels are
generally not high enough to be life threatening unless the user
has a pacemaker.
[0218] The conductive fabric can reduce costs by lowering total
part count. Because the conductive fabric connects all the sensor
nodes together to the sensor module, the need for multiple
batteries, Bluetooth antennas, microprocessors, and more are
eliminated because many of these parts are consolidated and placed
within the sensor module(s).
[0219] Configurations
[0220] The conductive fabric can range in thickness from 0.45 mm up
to 4 mm. The fabric's width can range from 6 mm to 15 mm, and total
length for each fabric (combined shirt and pants) may range from 1
m upwards, depending on the user's body lengths. For instance,
garment(s) for children could be as low as 1 m.
[0221] The conductive fabric includes one or more separate
conductive yarn or wire channels. It is important to isolate the
separate conductive yarn or wire channels from one another to avoid
short circuiting. It is also important to create a pattern that
maintains a constant distance between adjacent conductive yarn
channels. The composition and mixture of the conductive yarn or
wire as it relates to electrical characteristics is important.
Exemplary configurations characteristics follow.
[0222] Linear Distance: The distance between each conductive yarn
channel can range from 1 mm to 4 mm (center to center), preferably
2.3 mm (see FIG. 31). This regulates the distance of the pin(s) for
the sensor node(s) and sensor module(s). It is important to
maintain this linear distance or the pin(s) may not be able to make
a good electrical connection, which would render the garment(s)
defective or otherwise nonfunctional.
[0223] Resistance: The conductive yarn channel preferably does not
exceed 50 ohms per 1 m. Maintaining acceptable electrical
characteristics can directly affect signal attenuation and data
transmission rates. Too high of a resistance will result in poor
signal attenuation and low data transmission rates. To achieve the
required electrical characteristic specifications, resistivity can
be decreased by applying a pitch (twists per length) to bring the
individual yarn strands closer together. Whether the conductive
material is a fiber, metal, or yarn, this tactic is valid for all
conductive materials. In this manner, the conductive surfaces are
moved closer together and more within a smaller length. However,
the mechanical characteristics of a fiber compared with a metal are
significantly different that one material may cold work and break
during this type of packing, which presents a limitation to which
types of materials this tactic may be applied to. A preferable
pitch is 50 to 500 revolutions per meter.
[0224] Coating: The coating thickness can range from 0.25 mm to 2
mm. It is important to select a thickness that enables the coating
to remain fairly intact during high flexion or stretch conditions.
The thinner the material, the more likely that the coating will
fall off. However, the thicker the coating, the more likely the
user will experience discomfort.
[0225] Elongation: It is preferable to have a conductive fabric
that has elongation characteristics (along the warp) to be 100% to
120% with a 0.5 kg weight. It is not necessary for the conductive
fabric to have elongation characteristics greater than 20%
elongation in the weft direction, but it is acceptable. A material
with high elongation characteristics will provide a more
comfortable experience. Users generally do not prefer materials
that constrain movement or provide resistance, although this
depends on the application. For example, if the user is training,
they may prefer less elongation, as it adds more resistance.
However, for performance or competition events, less resistance may
be more beneficial to the user's performance.
[0226] Electroluminescent yarn: This yarn can be weaved or knitted
in the warp or weft. This material would require more power
consumption and other electrical circuitry such as an AC inverter.
This could be beneficial to include in the garment for general
notification purposes.
[0227] Placement: The electrically conductive fabric can be
situated along the length of the arms and legs. The electrically
conductive fabric would be situated along the length of the limbs
because typically the sensor node(s) are not directly connected, in
series, on the electrically conductive fabric. The locations of the
sensor node(s) drive the positioning of the electrically conductive
fabric (FIG. 23).
[0228] Materials
[0229] Various electrically conductive materials can be used,
depending on application and desired properties:
[0230] (1) Metal wire: Typical wire in the range of 20 gauge to 30
gauge, weaved or knitted in the warp of the electrically conductive
yarn, can be used. Typical metal wire, such as copper, brass, etc.,
has very high conductivity and low capacitance, which has excellent
signal transmission performance. Furthermore, wire with insulation
can also be used as well. However, the material is very fragile and
can cold work under repeated flexion and bending.
[0231] (2) Conductive metalized yarn: It is important to select
electrically conductive yarns that provide adequate electrical and
mechanical attributes. However, the electrical characteristics are
generally inferior to those of typical metal wires. It is important
to achieve electrical characteristics that avoid significant signal
attenuation between the sensor module(s) and the sensor node(s).
Moreover, it is important to provide a durable fabric structure. It
is also important to design a structure that is comfortable and can
be easily integrated into the garment. There are various
configurations of conductive yarn available for use, such as
polyamide 6.6 filament yarn, 99% silver yarn made by Shieldex,
denier 520/68f or other various deniers and numbers of ply.
Electrically conductive yarns are typically nylon (or other
suitable materials) yarn of varying denier and ply, which are
plated with conductive non-ferrous metals like copper or silver, or
ferrous metals like as stainless steel. Yarns mixed with strands of
metal wires or wire meshes with non-conductive yarn may provide
more desirable electrical performance.
[0232] Base yarn: Highly stretchable, resilient, and cost effective
materials, such as polyester or nylon, are preferable. The base
yarn is used in the warp and weft. This is a significant component
to the electrically conductive fabric as it is used to position and
regulate position and performance of the length, width, elongation,
etc. The denier of the base yarn can preferably be as small as
possible without adversely affecting mechanical and electrical
performance. Typically, small denier will be more comfortable to
the user because it will result in a thinner part and be less
intrusive to the user. The pick (or thread density) should be
sufficiently high such that mechanical performance is not
sacrificed. A smaller pick will allow the fabric to have more
elongation; however, the ability to maintain linear distance of
conductive materials is weakened.
[0233] Elastomer: The elastomer regulates stretch of the material
and acts as a component to bring elasticity into the fabric.
Preferable materials include neoprene, butadyl, latex, nitrile or
other suitable elastic materials. The elastomer regulates
elongation properties and enables the assembled electrically
conductive fabric to return to static length. The elastomer should
run along the entire length (parallel to the warp) of the
electrically conductive fabric, as it is an integral component of
the fabric. There may be one or more weft elastomers that run along
the length of the electrically conductive fabric. The diameter of
the elastomer ranges from 0.25 mm to 1 mm.
[0234] Manufacturing: Possible manufacturing methods include
knitting or weaving. The pattern for the knit should allow for
thin, yet highly stretchable designs. The electrically conductive
fabric is created on a specialized narrow fabric weaving machine or
a specialized knitting machine.
[0235] Finish/Appearance: The electrically conductive fabric will
be coated with a textile coating, which may come in a variety of
colors, including, but not limited to black, white, and many other
options. The textile coating should not be conductive and should
possess isolative characteristics to provide protection to the user
against electrical shock. It should be able to withstand a
plurality of wash and dry cycles as well as various athletic
movements and flexion of the sensor node(s), electrically
conductive fabric, and sensor module(s).
[0236] Installation method: The coating can be deposited based on
typical industry standards for textile coatings. Various methods
may include, for example, lamination, direct application of a
polymer onto the textile surface, or indirect application of a
polymer to the textile surface. The insulation methods can use
typical industry standard practices and machinery to apply the
textile coating.
[0237] Coating durability: It is preferable to utilize a coating
that is designed for textile applications, such that the coating
remains bonded to the fabric under harsh conditions, such as during
multiple machine washing and drying cycles, flexion, and
torsion.
[0238] Durometer: The softness (durometer) is preferably less than
40 Shore A. Softer is preferable for providing more compliance and
a stronger seal against contaminants when it is assembled between
the casing membrane and the pin(s) of the sensor node(s) and
module(s).
[0239] Tackiness: The coating should be sufficiently tacky to help
maintain and limit relative movement between the garment, sensors,
and the user's skin. This is important for sensor accuracy, as high
migration of the sensor relative to the calibration can have
adverse effects on the output of the feedback and measurement.
[0240] Insolation: The coating should fully cover and insulate the
conductive fabric to protect the conductive yarn against foreign
contaminants such as (for example)dirt, debris, sweat, liquid, or
any material that could cause short circuiting or harm/discomfort
to the user, and to hold the warp and weft of the yarns in place
with respect to all components during flexion, torsion, or
elongation. The coating helps regulate the position and can help
prevent the conductive yarns from short circuiting upon flexion,
torsion, or elongation conditions. Adequate thickness is important
to provide redundant sealing mechanisms between the pin(s), sensor
node case membrane, and circuitry. Furthermore, the same sealing
redundancy can be replicated for the pin(s), sensor module case
membrane, and circuitry.
[0241] Skin compliance: It is important that the textile coating
meet and exceed the standards and requirements of ISO 10993 for
skin contact healthcare applications relating to cytotoxicity, skin
irritation, and sensitization (allergic) potential.
[0242] Materials: mixtures of silicone, rubber, polymers,
thermoplastics, and other materials, which meet and exceed
standards and requirements of ISO 10993, are suited to adequately
coat the conductive fabric.
[0243] FIG. 31 is a top view of electrically conductive fabric. The
4 horizontal details are the conductive fabric. The distance of 2.3
mm is the distance between each yarn.
Assembly
[0244] The following assembly process is not intended to limit the
possibilities, but rather to provide one or more options for
assembly. There are variations of the overall assembly process and
combinations thereof. See, e.g., FIGS. 32, 33.
[0245] Bond Electrically Conductive Fabric to Panel(s)
[0246] (1) Cut and Sew. After the fabric and trim is sourced and
the fabric pattern template is created and approved for production,
the manufacturing process can begin. The pattern is an outline of
each component, such as the sleeve, the back, the chest, etc. These
components are often referred to as "panels" in the cut and sew
industry. Panels are cut from large sheets of fabric and placed in
a fabric cutting machine. The general outline of the panels is cut
according to a template; in the fabric industry, this is often
referred to as a "pattern." The pattern for the exemplary garments
discussed above is unique at least because of its size and the fit
of the assembled product.
[0247] The machines used during the process depend primarily on the
garment unit volume required for delivery. At low production
volume, workers typically cut garment panels using scissors from
large sheets of fabric. At high production volume, the panel
extraction process can be controlled by an automated laser cutting
machine that cuts panels from large sheets of fabric.
[0248] (2) Bonding Electrically Conductive Fabric. The electrically
conductive fabric can be attached to garment by an overlay, sewing,
or welding process. The electrically conductive fabric can be
placed on the interior or the exterior of the garment, but it does
not have an impact on the performance. The decision to place the
electrically conductive fabric on the interior or exterior would be
based in part on aesthetics and may depend on the applications. If
placed on the exterior, it may be beneficial for sensor alignment
purposes and awareness for the user. For a discrete appearance, it
may be beneficial to install the sensor node(s) and module(s) with
the bulk of the case membrane hidden on the interior side of the
garment(s).
[0249] (i) Overlaying. The conductive fabric can be glued, printed,
or screen printed onto the surface of the panel. Electrically
conductive fabric can be attached on one or more panels at the same
time. This is a preferable interface because it would be most
ergonomic and potentially the least labor intensive process (for
example: cheaper to manufacture). However, it could take more time
associated with dry time of the adhesive. The compound used to
adhere the electrically conductive fabric should be stretchable and
is preferably skin compliant per ISO 10993 standards.
[0250] The machines used in this process include fixtures used to
control placement of the electrically conductive fabric onto the
panel(s). Fixtures are used to place the electrically conductive
fabric on the arms, legs, and torso. Machines used to control the
amount of adhesive would be useful so as to not waste adhesive.
[0251] (ii) Sewing. The electrically conductive fabric can be sewn
onto the seam of the garment, adjoining two cuts together to make a
seam. The electrically conductive fabric can be overlaid onto of
the seam or juxtaposed to the seam. This would be less optimal than
overlaying the conductive fabric through adhesion because the
conductive fabric would likely be larger in width to accommodate
the placement of the thread that attaches the electrically
conductive fabric. This is true because the thread could penetrate
and create a leak path to the electrically conductive yarn, which
would compromise functionality and durability.
[0252] Machines used in this process include fixtures for
controlling placement of the electrically conductive fabric onto
the panel. Fixtures can be used to place the electrically
conductive fabric on the arms, legs, and torso. Typical production
sewing machines can be used for the assembly process.
[0253] (iii) Welding. The electronically conductive fabric may be
ultrasonically welded onto the interior or exterior of the panel.
This could prove advantageous because the installation and setup
time is quicker and less labor intensive than overlaying and
sewing. It may not appear to be most attractive aesthetically
because when the panel is welded to the electrically conductive
fabric, the surface of the weld may not be homogenous and can have
a rough surface finish.
[0254] Machines used in this process include fixtures for
controlling placement of the electrically conductive fabric onto
the panel. Fixtures can be used to place the electrically
conductive fabric on the arms, legs, and torso. Typical sonic
welding machines with specific fixtures can be used for the
assembly process.
[0255] (3) Sew sensor voids. Once the electrically conductive
fabric is installed onto the garment panels, the sensor voids are
created and sewn juxtaposed to the electrically conductive fabric.
The placement of these sensor voids corresponds with the location
of the sensor node and the sensor module. The placement of these
sensor voids can be critical to the accuracy of the measurements
taken by the sensor node and module. FIG. 34 shows a sensor void
example
[0256] Assemble Garment(s)
[0257] The panels can be sewn together using typical production
style sewing machines, with a worker sewing the panels together.
The worker can align each panel together and stitch accordingly.
This can involve sewing machines and some fixtures for aligning
panels together.
[0258] Assemble Node(s)
[0259] STEP 1: Gather parts (parts A, B, C, and D).
[0260] Step 2: Set part C into alignment fixture with void facing
up. The alignment fixture guides the assembly worker to orient the
part in the most ergonomic way.
[0261] Step 3: Align and place part D into void, making sure
conductive pads are visible.
[0262] Step 4: Align part B on top of parts D and C. The alignment
features built into part B will provide smooth engagement.
[0263] Step 5: Bond parts B and C together. Machines that can be
used in this process include a sonic welder, press, or clamping
mechanisms.
[0264] Assemble Band(s)
[0265] Step 1: Gather parts (parts E, F, G, I, and H)
[0266] Step 2: Set part E into alignment fixture with void facing
up. The alignment fixture can guide the assembly worker to orient
the part ergonomically way.
[0267] Step 3: Align and place parts F, G, and I into void.
[0268] Step 4: Align part H on top of parts E, F, G, and I. The
alignment features built into part H will provide smooth
engagement.
[0269] Step 5: Bond parts H and E together. Machines that can be
used in this process include a sonic welder, press, or clamping
mechanisms.
[0270] Step 6: Insert assembled parts E, F, G, H, and I into part
J.
[0271] Assemble Module(s)
[0272] Step 1: Gather parts (parts K, L, M, N, O, P, Q)
[0273] Step 2: Set Part N into alignment fixture with void facing
up. The alignment fixture guides the assembly worker to orient the
part in an ergonomic manner.
[0274] Step 3: Align and place parts M and O into void.
[0275] Step 4: Align part L on top of parts N, M, and O. The
alignment features built into part L will provide smooth
engagement
[0276] Step 5: Bond parts L and M together. [0277] Machines that
can be used in this process include a sonic welder, press, or
clamping mechanisms.
[0278] Assemble Node(s) and Module(s) to Garment(s)
[0279] (1) Assemble Sensor Nodes and Sensor Modules to Shirt and
Pants
[0280] The sensor node is installed on the interior or the exterior
of the garment. If placed on the exterior, it may be beneficial for
sensor alignment purposes and awareness for the user. For a
discrete appearance, it may be beneficial to install the sensor
node(s) and module(s) with the bulk of the case membrane hidden on
the interior side of the garment(s). The sensor node can be placed
on the exterior for design purposes. The electrically conductive
fabric coupled with the sensor nodes could highlight the garment
and provide for some marketing detail. It could also aid in better
placement of the sensors. In this format, it could also be somewhat
more comfortable for the user.
[0281] It may be preferable to place the holster and the sensor
module(s) on the exterior of the garment to enable easier access
for removal from and attachment to the garment.
[0282] (2) Final Sensor Node Installation
[0283] Step 1: Gather parts (bonded parts B and C and garment(s)
with electrically conductive fabric)
[0284] Step 2: Set bonded parts B and C into fixture with the
electrically conductive fabric interface remaining visible. There
will be a fixture that aligns bonded parts B and C in an
orientation that is ergonomic and aligns the sensor node for the
next step.
[0285] Step 3: Place garment(s) with electrically conductive fabric
on top of bonded parts B and C by using its alignment geometry.
Guide the pin(s) through the garment(s) with electrically
conductive fabric, penetrating the conductive channels.
[0286] Step 4: Guide the alignment features of part B through the
garment sensor void(s).
[0287] Step 5: Bond the bonded parts B and C with part A.
Additionally, glue, adhesives, epoxy, RTV, etc. may be applied
between the sensor node and sensor modules between the pin(s) and
the electrically conductive fabric to promote further sealing for a
multiple washing and drying cycles. This process may involve a
sonic welder, press, or clamping mechanisms.
[0288] (3) Final Sensor Module Installation.
[0289] Step 1: Gather parts (parts Q and K and garment(s) with
electrically conductive fabric)
[0290] Step 2: Set part K into fixture with the electrically
conductive fabric interface remaining visible. There will be a
fixture that aligns part K in an orientation that is ergonomic and
aligns the part for the next step.
[0291] Step 3: Place garment(s) with electrically conductive fabric
over part K. The alignment features and geometry of part K will
help align the garment(s) with bonded electrically conductive
fabric onto part K. Move the alignment geometry through the garment
sensor void(s).
[0292] Step 4: Align part Q using the alignment geometry of part K
and allow part Q to rest on top of the assembly.
[0293] Step 5: Bond parts K, Q, and the garment(s) with
electrically conductive fabric together. Additionally, glue,
adhesives, epoxy, RTV, etc. may be applied between the sensor node
and sensor modules between the pin(s) and the electrically
conductive fabric to promote further sealing for multiple washing
and drying cycles. Machines that may be used in this process
include a sonic welder, press, or clamping mechanisms.
[0294] Various preferred versions of the invention are shown and
described above to illustrate different possible features of the
invention and the varying ways in which these features may be
combined. Apart from combining the different features of the
foregoing versions in varying ways, other modifications are also
considered to be within the scope of the invention. The invention
encompasses at least all different versions that fall literally or
equivalently within the scope of the claims.
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