A Motion Classification Approach to Fall Detection

Shanfeng Hu, Worasak Rueangsirarak, Maxime Bouchee, Nauman Aslam, Hubert P.H. Shum

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The population of older people in the world has grown rapidly in recent years. To alleviate the increasing burden on health systems, automated health monitoring of older people can be very economical for requesting urgent medical support when a harmful accident has been detected. One of the accidents that happens frequently to older people in a household environment is a fall, which can cause serious injuries if not handled immediately. In this paper, we propose a motion classification approach to fall detection, by integrating the techniques of motion capture and machine learning. The motion of a person is recorded with a set of inertial sensors, which provides a comprehensive and structural description of body movements, while being robust to variations in the working environment. We build a database comprising motions of both falls and normal activities. We experiment with several combinations of joint selection, feature extraction, and classification algorithms, showing that accurate fall detection can be achieved by our motion classification approach.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2017
Subtitle of host publicationMalabe, Sri Lanka, 6-8 December 2017
Place of PublicationPiscataway, NJ
PublisherIEEE
ISBN (Electronic)9781538646021
ISBN (Print)9781538646038
DOIs
Publication statusPublished - Dec 2017
Event11th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2017 - Malabe, Sri Lanka
Duration: 6 Dec 20178 Dec 2017

Publication series

NameInternational Conference on Software, Knowledge Information, Industrial Management and Applications, SKIMA
Volume2017-December
ISSN (Print)2373-082X
ISSN (Electronic)2573-3214

Conference

Conference11th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2017
CountrySri Lanka
CityMalabe
Period6/12/178/12/17

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