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

Published on Jan 1, 2015in IEEE Journal of Biomedical and Health Informatics4.217
· DOI :10.1109/JBHI.2014.2313473
Saisakul Chernbumroong6
Estimated H-index: 6
(BU: Bournemouth University),
Shuang Cang17
Estimated H-index: 17
(BU: Bournemouth University),
Hongnian Yu27
Estimated H-index: 27
(BU: Bournemouth University)
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.
  • References (18)
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Assisted living systems can help support elderly persons with their daily activities in order to help them maintain healthy and safety while living independently. However, most current systems are ineffective in actual situation, difficult to use and have a low acceptance rate. There is a need for an assisted living solution to become intelligent and also practical issues such as user acceptance and usability need to be resolved in order to truly assist elderly people. Small, inexpensive and low...
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This paper presents a child activity recognition approach using a single 3-axis accelerometer and a barometric pressure sensor worn on a waist of the body to prevent child accidents such as unintentional injuries at home. Labeled accelerometer data are collected from children of both sexes up to the age of 16 to 29 months. To recognize daily activities, mean, standard deviation, and slope of time-domain features are calculated over sliding windows. In addition, the FFT analysis is adopted to ext...
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