Structural action recognition in body sensor networks: distributed classification based on string matching

Authors:
Hassan Ghasemzadeh;Vitali Loseu;Roozbeh Jafari
Affiliations:
Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX;Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX;Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX
Venue:
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Year:
2010

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Abstract

Mobile sensor-based systems are emerging as promising platforms for healthcare monitoring. An important goal of these systems is to extract physiological information about the subject wearing the network. Such information can be used for life logging, quality of life measures, fall detection, extraction of contextual information, and many other applications. Data collected by these sensor nodes are over whelming, and hence, an efficient data processing technique is essential. In this paper, we present a system using inexpensive, off-the-shelf inertial sensor nodes that constructs motion transcripts from biomedical signals and identifies movements by taking collaboration between the nodes into consideration. Transcripts are built of motion primitives and aim to reduce the complexity of the original data. We then label each primitive with a unique symbol and generate a sequence of symbols, known as motion template, representing a particular action. This model leads to a distributed algorithm for action recognition using edit distancewith respect to motion templates. The algorithm reduces the number of active nodes during every classification decision. We present our results using data collected from five normal subjects performing transitional movements. The results clearly illustrate the effectiveness of our framework. In particular, we obtain a classification accuracy of 84.13% with only one sensor node involved in the classification process.