Abstract
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 exploiting both the data sampled by accelerometer sensors carried out by the user and the reciprocal Received Signal Strength (RSS) values coming from worn wireless sensor devices and from sensors deployed in the environment. To this end, we model the accelerometer and the RSS stream, obtained from a Wireless Sensor Network (WSN), 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 deployment cost.
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Palumbo, F., Barsocchi, P., Gallicchio, C., Chessa, S., Micheli, A. (2013). Multisensor Data Fusion for Activity Recognition Based on Reservoir Computing. In: Botía, J.A., Álvarez-García, J.A., Fujinami, K., Barsocchi, P., Riedel, T. (eds) Evaluating AAL Systems Through Competitive Benchmarking. EvAAL 2013. Communications in Computer and Information Science, vol 386. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41043-7_3
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DOI: https://doi.org/10.1007/978-3-642-41043-7_3
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