Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people.
Journal:
IEEE journal of biomedical and health informatics
PMID:
24771599
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.
Authors
Keywords
Accelerometry
Actigraphy
Activities of Daily Living
Aged
Algorithms
Artificial Intelligence
Electrocardiography, Ambulatory
Female
Geriatric Assessment
Humans
Male
Models, Genetic
Motor Activity
Pattern Recognition, Automated
Reproducibility of Results
Sensitivity and Specificity
Subtraction Technique
Thermography
Transducers