Your body signals expose your fall

Eugene Yujun Fu, Cheuk Yin Wong, Katie T.Y. Lau, Hong Va Leong, Grace Ngai

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

Abstract

Fall is a common cause of severe injuries that may lead to irreversible body damage and even death. A real-time fall monitoring system can reveal a fall in time for timely medical aid to a victim. This is particularly important in the context of mobile healthcare. Fall detection with most contemporary wearable devices relied solely on acceleration signals, often not flexible and robust enough. In this paper, we propose to deploy body signals in a multi-modality approach. Besides the common acceleration signals, we also make use of physiological signals returned by wearable devices for multiple modalities. Fall detectionwould not fail easily even if some acceleration signals become ineffective. Our experiment results indicate that we are able to attain an accuracy of more than 96%. An in-depth evaluation demonstrates that physiological signals can contribute in distinguishing falls from actions generating similar acceleration signals, such as jumps, sit-downs and walking-downstairs.

Original languageEnglish
Title of host publication21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Proceedings
EditorsMaria Indrawan-Santiago, Eric Pardede, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Anderst-Kotsis
PublisherAssociation for Computing Machinery
Pages1-5
Number of pages5
ISBN (Electronic)9781450371797
DOIs
Publication statusPublished - 2 Dec 2019
Event21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Munich, Germany
Duration: 2 Dec 20194 Dec 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019
Country/TerritoryGermany
CityMunich
Period2/12/194/12/19

Keywords

  • Fall detection
  • Mobile health-care
  • Multi-modality approach
  • Physiological signals
  • Wearable sensor

ASJC Scopus subject areas

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

Cite this