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Transitional Activity Recognition with Manifold Embedding

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4 Author(s)
Atallah, L. ; Dept. of Comput., Imperial Coll. London, London, UK ; Ali, R. ; Guang-Zhong Yang ; Lo, B.

Activity monitoring is an important part of pervasive sensing, particularly for assessing activities of daily living for elderly patients and those with chronic diseases. Previous studies have mainly focused on binary transitions between activities, but have overlooked detailed transitional patterns. For patient studies, this transition period can be prolonged and may be indicative of the progression of disease. To observe, as well as quantify, transitional activities, a manifold embedding approach is proposed in this paper. The method uses a spectral graph partitioning and transition labelling approach for identifying principal and transitional activity patterns. The practical value of the work is demonstrated through laboratory experiments for identifying specific transitions and detecting simulated motion impairment.

Published in:

Wearable and Implantable Body Sensor Networks, 2009. BSN 2009. Sixth International Workshop on

Date of Conference:

3-5 June 2009