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

This source preferred by Hongnian Yu and Shuang Cang

Authors: Chernbumroong, S., Cang, S. and Yu, H.

Journal: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

This data was imported from DBLP:

Authors: Chernbumroong, S., Cang, S. and Yu, H.

Journal: IEEE J. Biomedical and Health Informatics

Volume: 19

Pages: 282-289

DOI: 10.1109/JBHI.2014.2313473

This data was imported from Scopus:

Authors: Chernbumroong, S., Cang, S. and Yu, H.

Journal: IEEE Journal of Biomedical and Health Informatics

Volume: 19

Issue: 1

Pages: 282-289

ISSN: 2168-2194

DOI: 10.1109/JBHI.2014.2313473

© 2014 IEEE. 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.

This data was imported from Web of Science (Lite):

Authors: Chernbumroong, S., Cang, S. and Yu, H.

Journal: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

Volume: 19

Issue: 1

Pages: 282-289

ISSN: 2168-2194

DOI: 10.1109/JBHI.2014.2313473

This data was imported from Europe PubMed Central:

Authors: Chernbumroong, S., Cang, S. and Yu, H.

Journal: IEEE journal of biomedical and health informatics

Volume: 19

Issue: 1

Pages: 282-289

eISSN: 2168-2208

ISSN: 2168-2194

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.

The data on this page was last updated at 04:46 on November 24, 2017.