5th International ICST Conference on Body Area Networks

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},
        keywords={body-area networks body-worn accelerometers smart cameras activity recognition},
  • Nicola Bicocchi
    Matteo Lasagni
    Marco Mamei
    Andrea Prati
    Rita Cucchiara
    Franco Zambonelli
    Year: 2012
    Unsupervised Learning in Body-Area Networks
    DOI: 10.1145/2221924.2221955
Nicola Bicocchi1, Matteo Lasagni1, Marco Mamei1,*, Andrea Prati1, Rita Cucchiara1, Franco Zambonelli1
  • 1: University of Modena and Reggio Emilia
*Contact email: mamei.marco@unimore.it


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.