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Personal Identification using Gait Data on Slipper-device with Accelerometer - Asian CHI 2021 Symposium

Personal Identification using Gait Data on Slipper-device with Accelerometer - Asian CHI 2021 Symposium

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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
• 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
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
• 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
9
Experiment:System
Learning
phase
Predict
phase
Data
Walking Feature
SVM
Classifier
System overview
Person
Prediction
result
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

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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