Personal Identification using Gait Data on Slipper-device with Accelerometer

Miyu Fujii, Kaho Kato, Chengshuo Xia, Yuta Sugiura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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 languageEnglish
Title of host publication5th Asian CHI Symposium 2021
EditorsJosh B. Tedjasaputra, Briane Paul V. Samson, Masitah Ghazali, Eunice Sari, Eunice Sari, Sayan Sarcar, Dilrukshi Gamage, Yohannes Kurniawan
PublisherAssociation for Computing Machinery, Inc
Number of pages6
ISBN (Electronic)9781450382038
Publication statusPublished - 2021 May 8
Event5th Asian CHI Symposium 2021 - Virtual, Online, Japan
Duration: 2021 May 72021 May 8

Publication series

Name5th Asian CHI Symposium 2021


Conference5th Asian CHI Symposium 2021
CityVirtual, Online


  • gait identification

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Human-Computer Interaction
  • Information Systems
  • Software


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