This document summarizes a seminar presentation about a wearable device for acquiring biometric data for sports applications. The device uses MEMS sensors to track movement and collect biometric information like pulse rate, temperature, and more. It is designed to be worn on the wrist and uses miniature sensors, data storage, and a battery to allow for continuous monitoring during activities like tennis matches. The data is analyzed both on the device and through cloud services to provide performance analysis and insights into an athlete's physical and mental condition.

1. A wearable devices biometric
data Acquisition
For sports applications
SEMINAR BY
ABHIL.M.R
CSE ROLL NO 2
REG NO:14413002 GUIDE:Ms.Sheeja Beevi.S
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INTRODUCTION
*Cost of MEMS reduced due to popularization of smart devices.
*Biometric data-Acceleration , angular speed , temperature pulse
rate etc.
*Miniaturization of sensors.
*Suitable for golf and Tennis.
*Tennis Stroke classification , heart rate and temperature
measured.

3. What it is?
•Miniature wearable device
•Detect and records
•Movement
-motion detection
-Inertial sensing
•Biometric information
-Biometric data acquisition
•Monitor
-skin temperature
-pulse rate
•sensor fusion
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4. Simple
process
Device
LiPo
Battery
Internet
COMPLEX
SIGNAL
ANALYSIS
Cloud service
6 HR autonomy
INDEX TERMS
*Biometric data acquisition
*Inertial Sensing
*Movement detection
*Pulse Rate
*Sensor fusion
Autonomy-a right/condition(self governing)
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5. SYSTEM ARCHITECTURE
•Movement Tracking
•Sensing of Biometric information
-Pulse Rate(PR)
-Pulse Rate Variability(PRV)
*Physical state
*Mental state
*effect of stress on body
SYSTEM
*Movement
*Biometric
information
Tracking
*Information
Visualization
*Performanc
e
Analysis
Miniature
Wearable
device 5A wearable devices biometric Data...
Cloud Service
*Big data
Oriented
Processing
&
Analysis

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Wearable Device Embedded Hardware Design
•Design light weight motion
•Wrist of athlete
•Device’s position right above the ulnar head.
PPG Measures
-PR
-PRV
PPG-Measuring technique during low movement intervals.

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Orientation of accelerometer axes
*Accelerometer facing towards us.
angular rate
+ve
Counter
clockwise
*If sampling rate of accelerometer&Gyroscopic < 1000 sps.
DEVICE-Standalone Manner
-Detects&classifies basic strokes in Real Time.
*if sampling rate > 1000 sps
-Detailed Stroke Analysis.
-upload to cloud service.
TENNIS MATCHES - 2HRS

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Physical Implementation
FR4 Printed Circuit Board(PCB)
Top Layer
*Micro USB controller
*Micro controller
*Battery charger
*IMU unit
*Flash memory
*LED for pulse sensing
*Power supply
*Temperature sensor
Bottom Layer
opposit
e *Push
Button
*RGB LED
Interfaces
*USB device
*ADC
*SPI
*I2 C

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*Sensing Hand motion
*Tri axis
accelerometer
*Tri axis
Gyroscope
6
Degrees(freedom)
[DOF]
*Accelerometer measuring ranges
+/-(2,4,8,16) G & 16-bit resolution/axis
*Gyroscope measures angular rate
+/-(125,245,500,1000,2000) dps & 16-bit resolution/axis
FIFO
IMU
Enables
Read
data
(Burst
mode)
memor
y
Integrated fifo
(8KB)
*reduce power
consumption.
MEMS IMU

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Max(accelerometer)=6664 SPS
Max(Gyroscope)=1666 SPS
MODE
Normal
Combo
mode
High
Performance
mode
operationa
l
0.9mA 1.2mA
High o/p data
rates
IMU
3mm
3mm
0.86mm
LGA-16L package
Suitable for miniature design

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Pulse
rate
sensor
Analog
Front
end PULSE
Low-noise
Receiver ADC
LED
Transmit
Section
Diagnostic
circuit
880nm/
660nm
LED
Fault
Detection
4 MHz
Clk
service
Silicon PIN
photodiode
i/p amplifier-ve
feedback
Zero
Bias
mode
Reflected
Light
sensing
430nm-1100nm
(photosensitive area)

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22-BIT
ADC
converter
SIGNA
L
Amplifier
100mA
8-bit resolution
Sampled
100 sps
*Amplifier-Differential transimpedance amplifier
Programmable Gain=10 v/mA-(4*103 v/mA)
Analog
Front End
DSBGA-36
package

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Temperature Sensing
*Contactless temeperature sensing.
*Infrared thermopile sensor with integrated math engine.
*Measures by absorbing passive infrared energy(40um-16um).
Math
Engine
Thermopile
combine
s
Change in
voltage
Internal
Cold-junction
referencecombine
s
Temperature
Sensor
*Sensor’s accuracy in the range
*Non volatile memory for strong calibration coefficients.
*temperature readings stored as 16-bit digital value.
*12c bus is used for communication with microcontroller.
Temperature
sensor
Reading every 5s
270 (normal mode)
2 (shutdown mode)
*low power
consumption
8-pin DSBGA package

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PC Application and cloud service

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Motion and Biometric data acquisition
*Motion Sensing
*Tracking
*Biometric Data acquisition

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Tennis stroke Detection
*Strokes: Forehand,backhand and serve.
*Serve – 16G amplitude.
*set up threshold amplitude> threshold=serve
*Derivative average of three axes
D[n]-Average derivative value
n –Sample data index
Ai-Acceleration sample of corresponding axis
i=1,x-axis
i=2,y-axis
i=3,z-axis
*Stroke detected when D[n] exceeds threshold.
*Threshold parameter is 300.
*Average acceleration derivative instead of actual acceleration
average.

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Tennis stroke classification
*Interval of observation of minimum and
maximum tracking is 50ms.
*Sample Buffer microcontroller’s RAM
*Algorithm executed on microcontroller
for 2385 clk cycles which is 74.5 at
clk frequency of 32 MHz.
*Duration of execution determined by
Internal cycle counter.

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Pulse rate and pulse rate variability
*Athlete’s mental and physical condition.
*Reflective photo plethysmography.
*for tissue illumination two light sources used RED and IR LED.
*R-R interval.

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Temperature Measurement
*Contactless thermopile temperature Sensor.
*Passive infrared energy of an object.
*Thermopile sensors detect temperature differentiats.

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CONCLUSION
*The device is designed to be worn on a wrist.
*Biometric data acquisition.
*Record temperature and pulse rate.
*Monitor arm movement and gestures.
*Benefit is health monitoring and sports
applications.
*In future it has more possibilities in medical
field
*Athlete’s psychophysical condition.
*contribute vital parameters.
*depth game performance analysis.

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REFERENCES
[2] H. Wang et al., ``Discriminative feature extraction via
multivariate linear
regression for SSVEP-based BCI,'' IEEE Trans. Neural Syst.
Rehabil.
Eng., vol. 24, no. 5, pp. 532541, May 2016.
[1]A Wearable Device and System for Movement and Biometric Data
Acquisition forSports Applications MARKO KOS AND IZTOK
KRAMBERGER, (Member, IEEE)
[3] Y. Zhang, G. Zhou, J. Jin, Q. Zhao, X. Wang, and A. Cichocki, ``Sparse
Bayesian classication of EEG for brain-computer interface,'' IEEE Trans.
Neural Netw. Learn. Syst., vol. 27, no. 11, pp. 22562267, Nov. 2016.

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Thank You!!!!