BSN-Based Activity Classification: A Low Complexity Windowing-&-Classification Approach

Abstract:

Wireless sensor networks (WSNs) are becoming more and more attractive because of their flexibility. In particular, WSNs are being applied to a user body in order to monitor and detect some activities of daily living (ADL) performed by the user (e.g., for medical purposes). This class of WSNs are typically denoted as body sensor networks (BSNs). In this paper, we discuss BSN-based human activity classification. In particular, the goal of our approach is to detect a sequence of activities, chosen from a limited set of fixed known activities, by observing the outputs generated by accelerometers and gyroscopes at the sensors placed over the body. In general, our framework is based on low-complexity windowing-&-classification. First, we consider the case of disjoint (in the time domain) activities; then, we extend our approach to encompass a scenario with consecutive non-disjoint activities. While in the first case windowing is separate from classification, in the second case windowing and classification need to be carried out jointly. The obtained results show a significant detection accuracy of the proposed method, making it suitable for healthcare monitoring applications.

• Periodical: Advances in Science and Technology (Volume 85)
• Pages: 53-58
• DOI: https://doi.org/10.4028/www.scientific.net/AST.85.53
• Online since: September 2012
• Authors: Matteo Giuberti, Gianluigi Ferrari
• Keywords: Activity Classification, Body Sensor Network, General Approach, Segmentation

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