Episode segmentation using recursive multiple eigenspaces

Authors:
Aziah Ali;Surapa Thiemjarus;Guang-Zhong Yang
Affiliations:
Department of Computing, Imperial College London, UK;School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thailand;Department of Computing, Imperial College London, UK
Venue:
EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
Year:
2009

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Abstract

Activity recognition is an important application of body sensor networks. To this end, accurate segmentation of different episodes in the data stream is a pre-requisite of subsequent pattern classification. Current techniques for this purpose tend to require specific supervised learning, thus limiting their general application to pervasive sensing applications. This paper presents an improved multiple eigenspace segmentation algorithm that addresses the common problem of under-segmentation in episode detection. Results show that the proposed algorithm significantly increases the segmentation accuracy when compared to existing methods.