Document - Multisensor data fusion for activity recognition based on reservoir computing

2013

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Multisensor data fusion for activity recognition based on reservoir computing

Palumbo F., Barsocchi P., Gallicchio C., Chessa S., Micheli A.

Activity Recognition WSN C.2 COMPUTER-COMMUNICATION NETWORKS Neural Networks Sensor Data Fusion AAL

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 Reser- voir Computing paradigm. Our results show that, with an appropriate configuration of the ESN, the system reaches a good accuracy with a low deployment cost.

Source: EvAAL 2013 - Evaluating AAL Systems Through Competitive Benchmarking. International Competitions and Final Workshop, pp. 24–35, Madrid-Valencia, Spain, July and September 2013

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@inproceedings{oai:it.cnr:prodotti:277699,
	title = {Multisensor data fusion for activity recognition based on reservoir computing},
	author = {Palumbo F. and Barsocchi P. and Gallicchio C. and Chessa S. and Micheli A.},
	doi = {10.1007/978-3-642-41043-7_3},
	booktitle = {EvAAL 2013 - Evaluating AAL Systems Through Competitive Benchmarking. International Competitions and Final Workshop, pp. 24–35, Madrid-Valencia, Spain, July and September 2013},
	year = {2013}
}