Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people.
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.
Saisakul
S
Chernbumroong
Shuang
S
Cang
Hongnian
H
Yu
D061725:Accelerometry / Q000295:instrumentation / Q000379:methods; D056044:Actigraphy / Q000295:instrumentation / Q000379:methods*; D000203:Activities of Daily Living; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D015716:Electrocardiography, Ambulatory / Q000295:instrumentation / Q000379:methods; D005260:Female; D015577:Geriatric Assessment / Q000379:methods*; D006801:Humans; D008297:Male; D008957:Models, Genetic; D009043:Motor Activity / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D013817:Thermography / Q000295:instrumentation / Q000379:methods; D014159:Transducers