Exercise recognition system using facial image information from a mobile device

Kaho Kato, Chengshuo Xia, Yuta Sugiura

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

Abstract

Daily exercise has played a significant role for people in staying healthy; however, some people cannot do moderate exercise continuously. In this paper, we proposed an exercise recognition system using facial image information to make daily exercise management convenient. The proposed system gets facial image information from a built-in camera on a mobile device and can recognize nine exercises by a support vector machine’s classifier. When a user faces the camera during the exercise, the system gets time-series data consisting of 62 features on the face. Via leave-one-subject-out cross-validation, the average classification accuracy reached up to 88.2%.

Original languageEnglish
Title of host publicationLifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages268-272
Number of pages5
ISBN (Electronic)9781665418751
DOIs
Publication statusPublished - 2021 Mar 9
Event3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021 - Nara, Japan
Duration: 2021 Mar 92021 Mar 11

Publication series

NameLifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies

Conference

Conference3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021
CountryJapan
CityNara
Period21/3/921/3/11

Keywords

  • Exercise measurement
  • Machine learning
  • Mobile devices

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health(social science)
  • Biochemistry
  • Artificial Intelligence
  • Computer Science Applications

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