Genetic Algorithm-Based Classifiers Fusion for Multisensor Activity Recognition of Elderly People

Chernbumroong, Saisakul; Cang, Shuang; Yu, Hongnian

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.

Citation

Chernbumroong, S., Cang, S., & Yu, H. (2015). Genetic Algorithm-Based Classifiers Fusion for Multisensor Activity Recognition of Elderly People. IEEE Journal of Biomedical and Health Informatics, 19(1), 282-289. https://doi.org/10.1109/jbhi.2014.2313473

• Journal Article Type: Article
• Online Publication Date: Apr 21, 2014
• Publication Date: 2015-01
• Deposit Date: Jun 15, 2022
• Journal: IEEE Journal of Biomedical and Health Informatics
• Print ISSN: 2168-2194
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 1
• Pages: 282-289
• DOI: https://doi.org/10.1109/jbhi.2014.2313473
• Keywords: Ambient intelligence, genetic algorithm (GA), neural networks, sensor fusion, smart homes, support vector machine (SVM)
• Public URL: http://researchrepository.napier.ac.uk/Output/2879297