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The intelligent habitat is made of fixed components (movements detectors and intelligent electric household appliances) and small mobile processors worn by the elder. Fixed and mobile components communicate to assist the elder in performing his tasks and to intervene in case of risk. The system has two types of features: those carried out inside the residence (information acquisition, cognitive help like sound or visual cues when everyday life activity is carried out in an incomplete or dangerous way) and those reporting to the relatives and the external care network major risk events or evolution of the elder health state. The system intervention with the elder must be personalized according to the incurred risk gravity, his health state, his life habits and his preferred interaction mode: image, text, sound, voice ...

Abstract-This paper presents an approach of feature extraction and classification for recognizing continuous dynamic gestures corresponding to Vietnamese Sign Language (VSL). Input data are captured by the depth sensor of a Microsoft Kinect, which is almost not affected by the light of environment. In detail, each gesture is represented by a volume corresponding to a sequence of depth images. The feature extraction stage is performed by dividing such volume into a 3D grid of same-size blocks in which each one is then converted into a scalar value. This step is followed by the process of classification. The wellknown method Support Vector Machine (SVM) is employed in this work, and the Hidden Markov Model (HMM) technique is also applied in order to provide a comparison on recognition accuracy. Besides, a dataset of 3000 samples corresponding to 30 dynamic gestures in VSL was created by 5 volunteers. The experiments on this dataset to validate the approach and that shows the promising results with average accuracy up to 95%.

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