Research Article
Unsupervised Learning in Body-Area Networks
@INPROCEEDINGS{10.1145/2221924.2221955, author={Nicola Bicocchi and Matteo Lasagni and Marco Mamei and Andrea Prati and Rita Cucchiara and Franco Zambonelli}, title={Unsupervised Learning in Body-Area Networks}, proceedings={5th International ICST Conference on Body Area Networks}, publisher={ACM}, proceedings_a={BODYNETS}, year={2012}, month={7}, keywords={body-area networks body-worn accelerometers smart cameras activity recognition}, doi={10.1145/2221924.2221955} }- Nicola Bicocchi
Matteo Lasagni
Marco Mamei
Andrea Prati
Rita Cucchiara
Franco Zambonelli
Year: 2012
Unsupervised Learning in Body-Area Networks
BODYNETS
ACM
DOI: 10.1145/2221924.2221955
Abstract
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 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.
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