SVM-Based Multi-Modal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms and First Experimental Results

Anthony Fleury 1, * Michel Vacher 2, * Norbert Noury 1
* Auteur correspondant
1 AFIRM
TIMC-IMAG - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble
Abstract : By 2050, about a third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international ADL or the French AGGIR scales, by automatically classifying the different Activities of Daily Living performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, Infra-Red Presence Sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs on postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors, is then used to classify each temporal frame into one of the activities of daily living that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing/undressing). This is done using Support Vector Machines.We performed a one-hour experimentation with 13 young and healthy subjects to determine the models of the different activities and then we tested the classification algorithm (cross-validation) with real data.
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IEEE Transactions on Information Technology in Biomedicine, Institute of Electrical and Electronics Engineers, 2010, 14 (2), pp. 274 - 283. 〈10.1109/TITB.2009.2037317〉
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Anthony Fleury, Michel Vacher, Norbert Noury. SVM-Based Multi-Modal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms and First Experimental Results. IEEE Transactions on Information Technology in Biomedicine, Institute of Electrical and Electronics Engineers, 2010, 14 (2), pp. 274 - 283. 〈10.1109/TITB.2009.2037317〉. 〈hal-00465076〉

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