Indian Journal of Science and Technology
Year: 2015, Volume: 8, Issue: Supplementary 9, Pages: 1-9
J. Sree Madhubala* and A. Umamakeswari
School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, India; [email protected], [email protected]
Among the elderly people, falls have become a common health problem. With a growing population of elderly people, health systems are needed to meet the necessities of elderly people. A Microsoft Kinect sensor monitors the usual activities of people and the acquired image frames are processed in Raspberry pi. The context aware feature extraction technique identify the shape of a person and a mean based classification distinguish the fall from usual activities, if it encounters the unusual activity then the alert is sent to the particular person’s caregivers through SIM800 GSM modem. Using the low cost Kinect sensor with Raspberry pi, action dataset is built that consist of three types of actions such as sitting, standing and falling for three different persons. The proposed computer vision approach accomplished the static background model which is resistant to the variations in illumination and provides better results.
Keywords: Canny Algorithm, Contour Approximation Method, Depth Image, Fall Detection Computer Vision
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