Unsupervised routine profiling in free-living conditions — Can smartphone apps provide insights?

Abstract

In activity recognition and behaviour profiling studies, wearable inertial sensors are commonly used to monitor the subjects' daily activities. However, the need of carrying the sensing devices in addition to personal belongings may prohibit the widespread use of the technologies. On the other hand, smartphones have become ubiquitous and most smartphones are already equipped with similar inertial sensors. Recent studies have proposed the use of smartphone for quantifying the activity and behaviour of the users. A smartphone based long-term routine profiling system is proposed. To simplify the user interface and facilitate the ubiquitous use of the system, unsupervised and optimized techniques have been developed and integrated into a mobile phone application. By running the application continuously in the background of the phone, the system captures and processes the sensing information to infer the activities of the users, and the results are forwarded to the server for profiling the routines using pattern mining techniques. The proposed system is validated through a study of six users over two weeks. The ability of the proposed system in capturing routine behavior is demonstrated in the results of the study.