Sitting Posture Recognition in Real-Time Combined with Index Map and BLS

The development of modern science and technology has seriously changed people's living and working habits. Bad sitting habit undoubtedly has an important impact on human health. Therefore, this paper proposes a real-time sitting posture recognition algorithm based on index map and BLS model. Firstly, use Kinect to collect body index maps and build self-built sitting posture database; then use SSIM to supervise the change of sitting posture, the BLS is used to recognize the category of sitting posture in real-time; finally, as for the excessive frames in video, this paper proposes double thresholds cascade algorithm to detect excessive frames. The experimental results show that the algorithm proposed can effectively recognize 8 types of sitting postures with the accuracy of 98.58%, the speed of 162fps and effectively detect the excessive frames.