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

Saisakul Chernbumroong, Shuang Cang, Hongnian Yu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)

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.

Original languageEnglish
Article number6803060
Pages (from-to)282-289
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Volume19
Issue number1
DOIs
Publication statusPublished - 21 Apr 2014

Fingerprint

Dive into the research topics of 'Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people'. Together they form a unique fingerprint.

Cite this