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Unsupervised Learning in Body-area Networks

Abstract: Pattern recognition is becoming a key application in bodyarea networks. This paper presents a framework promoting unsupervised training for multi-modal, multi-sensor classification systems. Specifically, it enables sensors provided with patter-recognition capabilities to autonomously supervise the learning process of other sensors. The approach is discussed using a case study combining a smart camera and a body-worn accelerometer. The body-worn accelerometer sensor is trained to recognize four user activities pairing accelerometer data with labels coming from the camera. Experimental results illustrate the applicability of the approach in different conditions.


Bicocchi, Nicola; Lasagni, Matteo; Mamei, Marco; Prati, Andrea; Cucchiara, Rita; Zambonelli, Franco "Unsupervised Learning in Body-area Networks" International ICST Conference on Body Area Networks, Corfu Island, pp. 164 -170 , September 10-12, 2010, 2010 DOI: 10.1145/2221924.2221955

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