Abstract
The number of elderly people requiring different levels of care in their homes has increased in recent times, with further increases expected. User studies show that the main concern of elderly people and their families is “fall detection and safe movement in the home”. We view abnormally long periods of inactivity as indicators of unsafe situations, and present a new method that models the tail of a hyperexponential distribution in order to reliably identify such inactivity periods from unintrusive sensor observations. The performance of our method was evaluated on two real-life datasets, and compared with that of a state-of-the-art technique, with our method outperforming this technique.
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Moshtaghi, M., Zukerman, I. (2014). Modeling the Tail of a Hyperexponential Distribution to Detect Abnormal Periods of Inactivity in Older Adults. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_85
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DOI: https://doi.org/10.1007/978-3-319-13560-1_85
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