Activity recognition by reservoir computing using signal streams produced by wireless sensor devices

Ambient Assisted Living facilities provide assistance and care for the elderly, where it is useful to infer their daily activity for ensuring their safety and successful ageing. In this work, we present an activity recognition system that classifies a set of common daily activities exploit- ing both the data sampled by accelerometer sensors carried out by the user, and the Receive Signal Strength (RSS) values coming from wireless sensors devices deployed in the environment. To this end, accelerometer and RSS streams, obtained from a Wireless Sensor Network (WSN), are treated using Recurrent Neural Networks implemented as efficient Echo State Networks (ESNs), within the Reservoir Computing paradigm. Our results show that, with an appropriate configuration of the ESN, the system reaches a good accuracy with a low energy cost.