Monitoring objects manipulations to detect abnormal behaviors

With a growing population of elderly people the number of subjects at risk of cognitive disorders is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects at their homes, reducing health-care costs and supporting medical diagnosis. Among the several behavioral aspects which clinicians are interested in monitoring, anomalous behaviors while performing activities of daily living are of great importance. In this work, we aim at improving the state of the art on this topic by enabling the recognition of fine-grained anomalies by detecting specific object manipulations. We attach tiny Bluetooth Low Energy accelerometers to several household objects in order to detect which manipulations are performed by the inhabitant on which object. Detected manipulations, combined with data from other environmental sensors deployed in the home, are used to infer ADLs and fine-grained abnormal behaviors. Preliminary results on a dataset with hundreds of complex activities captured in a smarthome environment show the effectiveness of the proposed method.