Simple and robust BSN-based activity classification: winning the first BSN Contest

Wireless sensor networks (WSNs) are becoming more and more attractive because of their flexibility. In particular, WSNs are being applied to a user body in order to monitor and detect some activities of daily living (ADL) performed by the user (e.g., for medical purposes). This class of WSNs are typically denoted as body sensor networks (BSNs). In this paper, we present a simple, yet accurate and robust, BSN-based activity classification algorithm that can detect and classify a sequence of activities, chosen from a limited set of fixed known activities, by observing the outputs generated by accelerometers and gyroscopes at the sensors placed over the body. This approach has led us to win the first BSN contest [1] and the presented results refer to the experimental data (publicly available) provided in this contest.