| Summary: | 博士 === 國立陽明大學 === 生物醫學工程學系 === 105 === The advancement in medicine and healthcare services have prolonged the life expectancy of the elderly and led the world population to be aging. Aging has become an important public health issue because of the degradation of physiological functions decreased the elder’s ability of independent living and this resulted in the elder’s activities of daily living (ADLs) require special supports and cares. This work aimed to introduce the ambient assisted living (AAL) into the home environment to improve the independency of elders. Through monitoring and supervising the ADLs, living supports and services can be provided to improve the elder’s independence of living and quality of life.
However, collecting the context information of ADLs is a time-consuming and difficult task. It is very challenging to perform continuing and long-term collection to obtain sufficient amount of data without influencing the normal ADLs of the elderly and inducing the privacy issue. In order to overcome the collecting difficulties, this study developed a technique to emulate the contexts of elder’s ADLs that can quickly simulate sufficient amount of data to provide a cost-effective solution. By implementing intelligent algorithm, the elder’s daily activities can be recognized and abnormal behavior can be detected.
In this work, the elder’s ADLs are simulated by using Poisson distribution and probability of state transition in which the ADLs contexts of 56 light-dependent elders are used as normal basis. Ten ADLs were selected and recognized by implementing Hidden Markov Models (HMMs). Support Vector Machine (SVM) was used to detect the abnormal behavior. According to the results, the techniques in activity recognition and abnormal behavior detection were successfully performed in the proposed architecture. The proposed healthcare infrastructure can be used to assist the caregivers and the family members to well understand the conditions of the residential elderly and provide adequate cares and supports to the elderly. The proposed methodology is applicable to be applied into home healthcare and monitoring system for the elderly. The normal behavior model and abnormalities detection mechanism provides various employment for the healthcare systems and enhances the quality of life for the elderly.
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