A dynamic motion pattern analysis approach to fall detection

In this paper we present our work on human body movement analysis, especially on fall detection. We have developed a reliable dynamic motion pattern analysis algorithm to detect fall situation. The algorithm works on the digital signal output from waist-mounted accelerometry. It first filters noisy components with a Gaussian filter; secondly sets up a 3D body motion model which relates various body postures to the outputs of accelerometry; finally a dynamic detection process is applied to make decision. Experiments were done on 40 cases mimicking various body movements. Our approach gave right judgements in all cases. Our work is an important part of elder care and rehabilitation.