IEEE J Biomed Health Inform - Fall detection based on body part tracking using a depth camera.

Tópicos

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Resumo

The elderly population is increasing rapidly all over the world. One major risk for elderly people is fall accidents, especially for those living alone. In this paper, we propose a robust fall detection approach by analyzing the tracked key joints of the human body using a single depth camera. Compared to the rivals that rely on the RGB inputs, the proposed scheme is independent of illumination of the lights and can work even in a dark room. In our scheme, a pose-invariant randomized decision tree algorithm is proposed for the key joint extraction, which requires low computational cost during the training and test. Then, the support vector machine classifier is employed to determine whether a fall motion occurs, whose input is the 3-D trajectory of the head joint. The experimental results demonstrate that the proposed fall detection method is more accurate and robust compared with the state-of-the-art methods.

Resumo Limpo

elder popul increas rapid world one major risk elder peopl fall accid especi live alon paper propos robust fall detect approach analyz track key joint human bodi use singl depth camera compar rival reli rgb input propos scheme independ illumin light can work even dark room scheme poseinvari random decis tree algorithm propos key joint extract requir low comput cost train test support vector machin classifi employ determin whether fall motion occur whose input d trajectori head joint experiment result demonstr propos fall detect method accur robust compar stateoftheart method

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