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User daily activity classification from accelerometry using feature selection and SVM

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. © 2009 Springer Berlin Heidelberg.

Año de publicación:

2009

Keywords:

    Fuente:

    scopusscopus

    Tipo de documento:

    Conference Object

    Estado:

    Acceso restringido

    Áreas de conocimiento:

    • Aprendizaje automático
    • Ciencias de la computación

    Áreas temáticas:

    • Ciencias de la computación

    Contribuidores:

    Rodríguez-Molinero A.Rodríguez-Molinero A.
    Cecilio Angulo BahónCecilio Angulo Bahón
    Parera J.Parera J.
    Cabestany J.Cabestany J.