Detection abnormal pattern in activities of daily living using sequence alignment method

As the aging is rapidly coming, the necessity of cares for old people increases. As requests for care services for patients requiring help of others increases, various systems for care services have been developed. For care services, recently, there have been developed technologies of tracking and monitoring daily activities of a person and recognizing the daily activities of the person by analyzing tracked data. Particularly, there have been developed systems for taking care of a person whose state should be periodically checked, such as patients or old persons. General care systems are good for tracking what activity the person executes, but limited to detecting what a person's state is, such as a normal or an abnormal state. So it is necessary to develop a new computational method to detect abnormal signs in a life pattern via changes of a sequence of activities of daily living.