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Title: A Bayesian circular changepoint method to identify changes in daily activity levels in the elderly Authors:  Simon Taylor - Lancaster University (United Kingdom) [presenting]
Rebecca Killick - Lancaster University (United Kingdom)
Abstract: According to Age UK there are 11.6m over 65's, 3.64m who live alone and over 25\% need help with at least one daily activity. A growing body of research indicates that changes in daily routine signal a change in health and well-being. Motivated by an industrial collaboration with Howz, we are using motion sensors in the home to automatically detect these changes as a key step in improving outcomes for elderly people and vulnerable members of society who live alone. Traditional changepoint methods to identify changes in activity levels view time as linear and thus are able to identify the day/night routine on a day-to-day basis. The typical assumption of independence of segments results in estimated changepoints and parameters that are unlikely to be consistent from day-to-day. As changes in routine happen on longer time scales, traditional methods make determining the normal daily patterns more challenging. We demonstrate a new changepoint method in the Bayesian framework that views time as circular in order to estimate the time-of-day changepoint events between different activity levels by pooling together information from across multiple days. These daily patterns can then be monitored for significant changes in daily changepoint locations and/or parameter estimates within segments.