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.
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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.
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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