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|dc.description.abstract||The effect of a fall towards an older person can be devastating and lead to loss of independence and reduce his/her quality of life. Furthermore, the cumulative effect of falls and resulting injuries can consume a disproportionate amount of health care resources. However, the number of physiotherapists is not sufficient to provide the necessary care for the increasing number of aging population. The governmental agencies try to solve the urgent problem by reducing the demand of the medical expert with the trained physiotherapist. This research outlines a Falling Risk Screening System to diagnose falling patterns in elderly people using Motion Capture Technology. The idea is to integrate an appropriate procedure including case based reasoning and motion capture to provide a decision support system. The diagnosis information derived from the process of case based reasoning helps support the physiotherapist to determine serious falling risks in the elderly and recommend guidelines for medical treatment. In this study, the limited sample data leads to use stratified 10-fold cross-validation method for performance evaluation of the CBR's retrieval mechanism. It demonstrates the very high performance, 81.67% of accuracy. © 2014 IEEE.||en_US|
|dc.title||Case-based reasoning system for screening falling risk of Thai elderly||en_US|
|article.title.sourcetitle||JICTEE 2014 - 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering||en_US|
|article.stream.affiliations||Mae Fah Luang University||en_US|
|article.stream.affiliations||Chiang Mai University||en_US|
|Appears in Collections:||CMUL: Journal Articles|
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