Title :
Classification of physical activities based on sparse representation
Author :
Shaopeng Liu ; Gao, Robert X. ; John, Deepak ; Staudenmayer, J. ; Freedson, P.S.
Author_Institution :
Dept. of Mech. Eng., Univ. of Connecticut, Storrs, CT, USA
fDate :
Aug. 28 2012-Sept. 1 2012
Abstract :
This paper presents a new classification method for physical activity assessment, based on sparse representation. This method bypasses the need for feature extraction and selection that is typically involved for activity classification, and classifies activities using raw sensor signals directly. Higher discriminative power than that from the conventional k-nearest neighbor algorithm has been demonstrated through experiments performed on 105 subjects.
Keywords :
biomechanics; feature extraction; medical signal processing; signal classification; signal representation; Higher discriminative power; classification method; conventional k-nearest neighbor algorithm; feature extraction; feature selection; physical activity assessment; raw sensor signals; sparse representation; Accuracy; Classification algorithms; Feature extraction; Legged locomotion; Sparse matrices; Training; Vectors; Algorithms; Humans; Motor Activity;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-4119-8
Electronic_ISBN :
1557-170X
DOI :
10.1109/EMBC.2012.6347410
