Support vector machine approach to fall recognition based on simplified expression of human skeleton action and fast detection of start key frame using torso angle

TLDR: A fall detection method for indoor environments based on the Kinect sensor and analysis of three-dimensional skeleton joints information is proposed and can be used in real-time video surveillance because of its time efficiency and robustness.

Abstract: Falls sustained by subjects can have severe consequences, especially for elderly persons living alone. A fall detection method for indoor environments based on the Kinect sensor and analysis of three-dimensional skeleton joints information is proposed. Compared with state-of-the-art methods, the authors' method provides two major improvements. First, possible fall activity is quantified and represented by a one-dimensional float array with only 32 items, followed by fall recognition using a support vector machine (SVM). Unlike typical deep learning methods, the input parameters of their method are dramatically reduced. Hence, videos are trained and recognised by an SVM with a low time cost. Second, the torso angle is imported to detect the start key frame of a possible fall, which is much more efficient than using a sliding window. Their approach is evaluated on the telecommunication systems team (TST) fall detection dataset v2. The results show that their approach achieves an accuracy of 92.05%, better than other typical methods. According to the characters of machine learning, when more samples are imported, their method is expected to achieve a higher accuracy and stronger capability of fall-like discrimination. It can be used in real-time video surveillance because of its time efficiency and robustness.