User Daily Activity Classification from Accelerometry Using Feature Selection and SVM

Authors:
Jordi Parera;Cecilio Angulo;A. Rodríguez-Molinero;Joan Cabestany
Affiliations:
CETpD. Technical Research Centre for Dependency Care and Autonomous Living, UPC. Technical University of Catalonia, Vilanova i la Geltrú, Spain 08800;CETpD. Technical Research Centre for Dependency Care and Autonomous Living, UPC. Technical University of Catalonia, Vilanova i la Geltrú, Spain 08800;CETpD. Mobility and Gait Lab, FHCSAA. Sant Antoni Abad Hospital, Vilanova i la Geltrú, Spain 08800;CETpD. Technical Research Centre for Dependency Care and Autonomous Living, UPC. Technical University of Catalonia, Vilanova i la Geltrú, Spain 08800
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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Abstract

User daily activity monitoring is useful for physicians in geriatrics and rehabilitation as a indicator of user health and mobility. Real time activities recognition by means of a processing node including a triaxial accelerometer sensor situated in the user's chest is the main goal for the presented experimental work. A two-phases procedure implementing features extraction from the raw signal and SVM-based classification has been designed for real time monitoring. The designed procedure showed an overall accuracy of 92% when recogninzing experimentation performed in daily conditions.