Activity Recognition and Stress Detection via Wristband

Johnny Chun Yiu Wong, Jun Wang, Eugene Yujun Fu, Hong Va Leong, Grace Ngai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Advancement of micro-electromechanical systems enables easy daily activity and physiological data collection with a smart wristband and smartphone. Making use of those signals in various intelligent algorithm can contribute much to trending m-health applications. The ability of continuously monitoring physical activities and stress level can help users to better track their health condition. In this study, we propose to recognize different physical activities and detect long lasting stress level based on the 3-axis acceleration signals and physiological signals. We are able to achieve accuracy of around 97% for physical activities recognition and more than 80% for stress detection. We also discover that physiological signals alone cannot distinguish well between the high intensity activities and the stress condition.

Original languageEnglish
Title of host publication17th International Conference on Advances in Mobile Computing and Multimedia, MoMM2019 - Proceedings
EditorsPari Delir Haghighi, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Anderst-Kotsis
PublisherAssociation for Computing Machinery
Pages102-106
Number of pages5
ISBN (Electronic)9781450371780
DOIs
Publication statusPublished - 2 Dec 2019
Event17th International Conference on Advances in Mobile Computing and Multimedia, MoMM2019 - Munich, Germany
Duration: 2 Dec 20194 Dec 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference17th International Conference on Advances in Mobile Computing and Multimedia, MoMM2019
Country/TerritoryGermany
CityMunich
Period2/12/194/12/19

Keywords

  • electrodermal activity
  • mHealth
  • physical activity
  • Stress
  • wearable device

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this