title: Unsupervised daily routine and activity discovery in smart homes.
abstract: The ability to accurately recognize daily activities of residents is a core premise of smart homes to assist with remote health monitoring. Most of the existing methods rely on a supervised model trained from a preselected and manually labeled set of activities, which are often time-consuming and costly to obtain in practice. In contrast, this paper presents an unsupervised method for discovering daily routines and activities for smart home residents. Our proposed method first uses a Markov chain to model a resident's locomotion patterns at different times of day and discover clusters of daily routines at the macro level. For each routine cluster, it then drills down to further discover room-level activities at the micro level. The automatic identification of daily routines and activities is useful for understanding indicators of functional decline of elderly people and suggesting timely interventions.
authors.lastname: Jie Yin
authors.lastname: Qing Zhang
authors.forename: Mohan
authors.initials: M
authors.lastname: Karunanithi
mesh_terms: D000203:Activities of Daily Living; D006699:Home Care Services; D006801:Humans; D008390:Markov Chains; D018670:Monitoring, Ambulatory