Natthapon Pannurat

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Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic(More)
This study presents a detailed summary of automatic fall detection system based on wearable sensor(s) and a real-time fall detection system prototype developed based on Java Expert System Shell (JESS), featuring the fall, fall recovery, fall direction and activity status before/after fall. Through the rule sets in the knowledge base, we illustrate how the(More)
This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Nineteen features are extracted, and the feature importance is measured by(More)
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