Unsupervised Learning In Body-Area Networks

Nicola Bicocchi, Matteo Lasagni, Marco Mamei, Andrea Prati, Rita Cucchiara, Franco Zambonelli


Pattern recognition is becoming a key application in body-area networks. This paper presents a framework promoting unsupervised training for multi-modal, multi-sensor classification systems. Specifically, it enables sensors provided with pattern-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. © 2010 ACM.

TitelBodyNets '10 Proceedings of the Fifth International Conference on Body Area Networks Pages 164-170
Herausgeber (Verlag)ACM
ISBN (Print)978-193696830-5
PublikationsstatusVeröffentlicht - 01.12.2011
Veranstaltung5th International ICST Conference on Body Area Networks
- Corfu, Griechenland
Dauer: 10.09.201012.09.2010
Konferenznummer: 90561


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