Personal Identification using Gait Data on Slipper-device with Accelerometer

Miyu Fujii; Kaho Kato; Chengshuo Xia; Yuta Sugiura

doi:10.1145/3429360.3468185

Personal Identification using Gait Data on Slipper-device with Accelerometer

Miyu Fujii, Kaho Kato, Chengshuo Xia, Yuta Sugiura

Department of Information and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

In this paper we presented a method of gait identification by slippers with an accelerometer to perform privacy-friendly personal identification. The gait data from accelerometer during walking is adopted from developed slipper devices as the personal unique data used for identification. Gait data is processed by Fast Fourier Transform to extract the frequency features and the Support vector machine (SVM) is used to identify the subject. Through assessing the different segmentation window size and various sensor positions, the results showed that an average accuracy was 95.0% using six sensors, and an average accuracy of 93.3% using three sensors placed at optimal positions.

Original language	English
Title of host publication	5th Asian CHI Symposium 2021
Editors	Josh B. Tedjasaputra, Briane Paul V. Samson, Masitah Ghazali, Eunice Sari, Eunice Sari, Sayan Sarcar, Dilrukshi Gamage, Yohannes Kurniawan
Publisher	Association for Computing Machinery, Inc
Pages	74-79
Number of pages	6
ISBN (Electronic)	9781450382038
DOIs	https://doi.org/10.1145/3429360.3468185
Publication status	Published - 2021 May 8
Event	5th Asian CHI Symposium 2021 - Virtual, Online, Japan Duration: 2021 May 7 → 2021 May 8

Publication series

Name	5th Asian CHI Symposium 2021

Conference

Conference	5th Asian CHI Symposium 2021
Country/Territory	Japan
City	Virtual, Online
Period	21/5/7 → 21/5/8

Keywords

gait identification

ASJC Scopus subject areas

Computer Networks and Communications
Human-Computer Interaction
Information Systems
Software

Access to Document

10.1145/3429360.3468185

Cite this

Fujii, M., Kato, K., Xia, C., & Sugiura, Y. (2021). Personal Identification using Gait Data on Slipper-device with Accelerometer. In J. B. Tedjasaputra, B. P. V. Samson, M. Ghazali, E. Sari, E. Sari, S. Sarcar, D. Gamage, & Y. Kurniawan (Eds.), 5th Asian CHI Symposium 2021 (pp. 74-79). (5th Asian CHI Symposium 2021). Association for Computing Machinery, Inc. https://doi.org/10.1145/3429360.3468185

Personal Identification using Gait Data on Slipper-device with Accelerometer. / Fujii, Miyu; Kato, Kaho; Xia, Chengshuo et al.
5th Asian CHI Symposium 2021. ed. / Josh B. Tedjasaputra; Briane Paul V. Samson; Masitah Ghazali; Eunice Sari; Eunice Sari; Sayan Sarcar; Dilrukshi Gamage; Yohannes Kurniawan. Association for Computing Machinery, Inc, 2021. p. 74-79 (5th Asian CHI Symposium 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fujii, M, Kato, K, Xia, C & Sugiura, Y 2021, Personal Identification using Gait Data on Slipper-device with Accelerometer. in JB Tedjasaputra, BPV Samson, M Ghazali, E Sari, E Sari, S Sarcar, D Gamage & Y Kurniawan (eds), 5th Asian CHI Symposium 2021. 5th Asian CHI Symposium 2021, Association for Computing Machinery, Inc, pp. 74-79, 5th Asian CHI Symposium 2021, Virtual, Online, Japan, 21/5/7. https://doi.org/10.1145/3429360.3468185

Fujii, Miyu ; Kato, Kaho ; Xia, Chengshuo et al. / Personal Identification using Gait Data on Slipper-device with Accelerometer. 5th Asian CHI Symposium 2021. editor / Josh B. Tedjasaputra ; Briane Paul V. Samson ; Masitah Ghazali ; Eunice Sari ; Eunice Sari ; Sayan Sarcar ; Dilrukshi Gamage ; Yohannes Kurniawan. Association for Computing Machinery, Inc, 2021. pp. 74-79 (5th Asian CHI Symposium 2021).

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title = "Personal Identification using Gait Data on Slipper-device with Accelerometer",

abstract = "In this paper we presented a method of gait identification by slippers with an accelerometer to perform privacy-friendly personal identification. The gait data from accelerometer during walking is adopted from developed slipper devices as the personal unique data used for identification. Gait data is processed by Fast Fourier Transform to extract the frequency features and the Support vector machine (SVM) is used to identify the subject. Through assessing the different segmentation window size and various sensor positions, the results showed that an average accuracy was 95.0% using six sensors, and an average accuracy of 93.3% using three sensors placed at optimal positions.",

keywords = "gait identification",

author = "Miyu Fujii and Kaho Kato and Chengshuo Xia and Yuta Sugiura",

note = "Funding Information: This work was supported by JST AIP-PRISM Grant Number JP-MJCR18Y2. Publisher Copyright: {\textcopyright} 2021 ACM.; 5th Asian CHI Symposium 2021 ; Conference date: 07-05-2021 Through 08-05-2021",

year = "2021",

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doi = "10.1145/3429360.3468185",

language = "English",

series = "5th Asian CHI Symposium 2021",

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N2 - In this paper we presented a method of gait identification by slippers with an accelerometer to perform privacy-friendly personal identification. The gait data from accelerometer during walking is adopted from developed slipper devices as the personal unique data used for identification. Gait data is processed by Fast Fourier Transform to extract the frequency features and the Support vector machine (SVM) is used to identify the subject. Through assessing the different segmentation window size and various sensor positions, the results showed that an average accuracy was 95.0% using six sensors, and an average accuracy of 93.3% using three sensors placed at optimal positions.

AB - In this paper we presented a method of gait identification by slippers with an accelerometer to perform privacy-friendly personal identification. The gait data from accelerometer during walking is adopted from developed slipper devices as the personal unique data used for identification. Gait data is processed by Fast Fourier Transform to extract the frequency features and the Support vector machine (SVM) is used to identify the subject. Through assessing the different segmentation window size and various sensor positions, the results showed that an average accuracy was 95.0% using six sensors, and an average accuracy of 93.3% using three sensors placed at optimal positions.

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ER -

Personal Identification using Gait Data on Slipper-device with Accelerometer

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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