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Transitional Activity Recognition with Manifold Embedding Raza Ali, Louis Atallah, Benny Lo and Guang-Zhong Yang
 

Summary: Transitional Activity Recognition with Manifold Embedding
Raza Ali, Louis Atallah, Benny Lo and Guang-Zhong Yang
Department of Computing and Institute of Biomedical Engineering
Imperial College London, SW7 2AZ
London, United Kingdom
e-mail: {smrali, latallah, benlo, gzy}@ doc.ic.ac.uk
Abstract-- 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

  

Source: Atallah, Louis - Department of Computing, Imperial College, London

 

Collections: Computer Technologies and Information Sciences