Challenges in Studying Falls of Community-Dwelling Older Adults in the Real World

Xin Hu, Rahav Dor, Steven Bosch, Anita Khoong, Jing Li, Susan Stark, Chenyang Lu

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

4 Scopus citations

Abstract

Despite over a decade of research and development in fall detection systems, accurate and reliable systems in use are few. The existing fall detection approaches leave three major challenges unsolved: (1) insufficient fall data for model training process, (2) unreliable labeling of ground truth, and (3) resorting to artificial falls to model falls. In this paper we highlight these challenges in a clinical study with community-dwelling adults. The data collected from the real world reveal significant differences between artificial falls and actual falls, and also to illuminate the limitations of existing algorithms. We further make recommendations for future work, based on the challenges, experience, and lessons we learned from this study.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509065172
DOIs
StatePublished - Jun 12 2017
Externally publishedYes
Event2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017 - Hong Kong, China
Duration: May 29 2017May 31 2017

Publication series

Name2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017

Other

Other2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017
Country/TerritoryChina
CityHong Kong
Period5/29/175/31/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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