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.

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

5. 9
Experiment：System
Learning
phase
Predict
phase
Data
Walking Feature
SVM
Classifier
System overview
Person
Prediction
result

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