Statistical models for unobtrusively detecting abnormal periods of inactivity in older adults

The number of elderly people requiring different levels of care in their home 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 house”, while eschewing intrusive monitoring devices. We view abnormally long periods of inactivity as indicators of unsafe situations, and present three models of the distribution of inactivity periods obtained from unintrusive sensor observations. The performance of these models was evaluated on two real-life datasets, and compared with that of a state-of-the-art system, with our models outperforming this system.