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.
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Ali, A., Thiemjarus, S., Yang, GZ. (2009). Episode Segmentation Using Recursive Multiple Eigenspaces. In: Barnaghi, P., Moessner, K., Presser, M., Meissner, S. (eds) Smart Sensing and Context. EuroSSC 2009. Lecture Notes in Computer Science, vol 5741. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04471-7_1
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DOI: https://doi.org/10.1007/978-3-642-04471-7_1
Publisher Name: Springer, Berlin, Heidelberg
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