Personal Identification using Gait Data on Slipper-device with Accelerometer - Asian CHI 2021 Symposium
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This document describes research on using gait data from a wearable accelerometer device in slippers to identify individuals. An experiment was conducted with 10 participants wearing slippers with 6-axis accelerometers. Gait data was collected from different sensor positions and used to train an SVM classifier to identify participants. The results showed over 94% accuracy when using data from the foot sensors, especially the toe, inner foot, and heel sensors. Additional experiments varying the frequency components and number of sensors showed combining data from multiple foot sensors can improve identification accuracy. The research aims to enable contactless personal identification in facilities while preserving privacy.
![• IMU(Inertial Measurement Unity)based walking
dataset[7] for identification
13
Experiment１：Overview
Participant 10（Male 5・Female 5）
Motion Walking
Sensor position Full body 17 places
Frame rate [fps] 60
Length[s] 90seconds
Number of point 5000
Samples 128
Window size 120
Dataset Overview
Sensor location
[7] C. Xia and Y. Sugiura, "Wearable Accelerometer Optimal Positions for Human Motion Recognition," 2020 IEEE 2nd Global Conference on Life Sciences and
Technologies (LifeTech), Kyoto, Japan, 2020, pp. 19-20](https://image.slidesharecdn.com/acmiyufujii-210524094313/85/Personal-Identification-using-Gait-Data-on-Slipper-device-with-Accelerometer-Asian-CHI-2021-Symposium-9-320.jpg)
![• Optimumal sensor position assessment using a
one-feet slipper device
15
Experiment２：Overview
Participant 5（Male 2・Female 3）
Motion
Walking
（Do not indicate the speed or
step）
Environment long flat corridor
Sensed data
6 accelerometers on the right
foot
（Same slipper on the left foot）
Frame rate[fps] 37.5
Length[s] 32
Data points 1200
Samples 128
Window size 100
Overview
Walking status](https://image.slidesharecdn.com/acmiyufujii-210524094313/85/Personal-Identification-using-Gait-Data-on-Slipper-device-with-Accelerometer-Asian-CHI-2021-Symposium-11-320.jpg)
![• Accuracy using all sensors (6 locations) is 95%
16
Result & Analysis：Accuracy from all
sensors
All six-sensor based confusion matrix[%]
Six-sensor](https://image.slidesharecdn.com/acmiyufujii-210524094313/85/Personal-Identification-using-Gait-Data-on-Slipper-device-with-Accelerometer-Asian-CHI-2021-Symposium-12-320.jpg)
Personal Identification using Gait Data on Slipper-device with Accelerometer - Asian CHI 2021 Symposium
- 1. Personal Identification using Gait Data on Slipper-device with Accelerometer 2021 Asian CHI Symposium M i y u F u j i , K e i o U n i v e r s i t y K a h o K a t o , K e i o U n i v e r s i t y C h e n g s h u o X i a , K e i o U n i v e r s i t y Yu t a S u g i u r a , K e i o U n i v e r s i t y
- 2. • Personal identification in entry / exit checks of indoor facilities (elderly housing with care, community centre, etc.) is significant; • Understand the facility usage • Reducing the burden on staff and users 2 Background: People gathering
- 3. 3 Background: Personal Identification ・Use face and appearance for identification ・Actions for identification; Look, touch, etc… ・ Use behavioural characteristics to identify ・Identified in daily activities Burden on the user is small Physical biometrics Behavioural biometrics Knowledge based Property based Biometrics based ・Key，IC-Card…etc ・Risk of theft or loss ・ID，password…etc ・Risk of leakage and forgetting
- 4. • Wearable device measures walking movement in the facility • Identify individuals in consideration of privacy →Incorporating a sensor in slippers 8 Methodology Daily used in indoor． Easy to wear． →Do not invade users' daily life Slippers installed at the entrance of the facility
- 6. 10 Experiment：Feature extraction Window based segment Hamming window applied FFT ( FFT ) Cut out only half feature quantity ． SVM classifier Uniform the segmented data Workflow of classifier building Cross-validation
- 7. 11 Experiment：hardware Device specifications Mounted device and sensor arrangement and their direction (6-sensor) Sensor 3-axis accelerometer Wireless module Xbee MCU Arduino Pro Mini
- 8. 12 Evaluation Protocol Experiment１：Validation of foot-based indentation • Personal identification with gait dataset Experiment２：Single feet based identification • Single data used, and considered the optimal sensor placement
- 9. • IMU(Inertial Measurement Unity)based walking dataset[7] for identification 13 Experiment１：Overview Participant 10（Male 5・Female 5） Motion Walking Sensor position Full body 17 places Frame rate [fps] 60 Length[s] 90seconds Number of point 5000 Samples 128 Window size 120 Dataset Overview Sensor location [7] C. Xia and Y. Sugiura, "Wearable Accelerometer Optimal Positions for Human Motion Recognition," 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), Kyoto, Japan, 2020, pp. 19-20
- 10. 14 Result & Discussion Sensor position and accuracy • The closer to the leg, the higher the accuracy is． • Right feet 94.3 %，left feet 95.3 %，both foot 97.0 ％ • Foot based personal identification is possible
- 11. • Optimumal sensor position assessment using a one-feet slipper device 15 Experiment２：Overview Participant 5（Male 2・Female 3） Motion Walking （Do not indicate the speed or step） Environment long flat corridor Sensed data 6 accelerometers on the right foot （Same slipper on the left foot） Frame rate[fps] 37.5 Length[s] 32 Data points 1200 Samples 128 Window size 100 Overview Walking status
- 12. • Accuracy using all sensors (6 locations) is 95% 16 Result & Analysis：Accuracy from all sensors All six-sensor based confusion matrix[%] Six-sensor
- 13. 17 Result & Analysis : Accuracy for each sensor Sensor position and identification accuracy Sensor position and name Toe Inner Front (IF) Outer Front (OF) Inner Back (IB) Outer Back (OB) Heel
- 14. 18 Result & Analysis：More sensors used Sensor combination and accuracy 93.3 % 88.3 % 88.3 % 93.3 % 91.7 % 91.7 % 91.7 %
- 15. 19 Result & Analysis : Frequency domain Change in frequency used • Low frequency components may be highly dependent on walking speed • Considering the high frequency components • Calculating identification accuracy by continuously reducing the frequency range used from the low frequency side Sensor placement
- 16. 20 Result & Analysis : Frequency domain . Comparison by the number of sensors of average identification accuracy when the frequency range used is changed →Combine the 3 sensors, better accuracy is expected.
- 17. 21 Limitation and Future work • Only the person registered as a data set can be identified. • New users need to get data for learning →Proposal of a method to register a person who does not exist on the dataset • Only for straight-line walking on a flat surface． • Data is acquired even in a state other than walking ． • Cannot identify movements other than walking, such as going up and down stairs ． → Combination with motion identification
- 18. Thank you! 2021 Asian CHI Symposium