Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people

Chernbumroong, S, Cang, S ORCID: https://orcid.org/0000-0002-7984-0728 and Yu, H, 2015. Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people. IEEE Journal of Biomedical and Health Informatics, 19 (1), pp. 282-289. ISSN 2168-2208

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Abstract

Activity recognition of an elderly person can be used to provide information and intelligent services to health care professionals, carers, elderly people, and their families so that the elderly people can remain at homes independently. This study investigates the use and contribution of wrist-worn multisensors for activity recognition. We found that accelerometers are the most important sensors and heart rate data can be used to boost classification of activities with diverse heart rates. We propose a genetic algorithm-based fusion weight selection (GAFW) approach which utilizes GA to find fusion weights. For all possible classifier combinations and fusion methods, the study shows that 98% of times GAFW can achieve equal or higher accuracy than the best classifier within the group.

Item Type:	Journal article
Publication Title:	IEEE Journal of Biomedical and Health Informatics
Creators:	Chernbumroong, S., Cang, S. and Yu, H.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	January 2015
Volume:	19
Number:	1
ISSN:	2168-2208
Identifiers:	Number Type 10.1109/jbhi.2014.2313473 DOI 1357057 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	27 Aug 2020 09:02
Last Modified:	31 May 2021 15:17
URI:	https://irep.ntu.ac.uk/id/eprint/40536

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