Paper Info
Title
Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people.
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.
Year
DOI
Venue
2015
10.1109/JBHI.2014.2313473
IEEE journal of biomedical and health informatics
Keywords
Field
DocType
biomedical telemetry,smart homes,boost classification,neural networks,ambient intelligence,intelligent services,support vector machine (svm),genetic algorithm-based classifiers fusion,health care professionals,geriatrics,medical signal processing,body sensor networks,genetic algorithm-based fusion weight selection,signal classification,genetic algorithms,fusion weights,accelerometers,wrist-worn multisensors,elderly people,genetic algorithm (ga),multisensor activity recognition,support vector machines,diverse heart rates,sensor fusion,accuracy,feature extraction
Health care,Activity recognition,Computer science,Feature extraction,Speech recognition,Artificial intelligence,Classifier (linguistics),Machine learning,Genetic algorithm
Journal
Volume
Issue
ISSN
19
1
2168-2208
Citations 
PageRank 
References 
16
0.59
9
Authors
3
Name
Order
Citations
PageRank
Saisakul Chernbumroong11315.76
Shuang Cang219016.48
hongnian339146.50