Contactless abnormal gait detection.

We present a new method to detect abnormal gait based on the symmetry verification of the two-leg movement. Unlike other methods requiring special motion captors, the proposed method uses image processing techniques to correctly track leg movement. Our method first divides each leg into upper and lower parts using anatomical knowledge. Then each part is characterised by two straight lines approximating its two borders. Finally, leg movement is represented by the angle evolution of these lines. In this process, we propose a new line approximation algorithm which is robust to the outliers caused by incorrect separation of leg into upper / lower parts. In our experiment, the proposed method got very encouraging results. With 281 normal / abnormal gait videos of 9 people, this method achieved a classification accuracy of 91%.