Estimating Human Pose with both Physical and Physiological Constraints

Abstract

With the constant improvement and perfection in theories of rehabilitation exercises, the industrial chain of rehabilitation medical big data, which combines computer vision, sensors, human-computer interaction, and other advanced technologies, is developing rapidly. As an important way to understand and analyze human behavior, human pose estimation plays an important role in helping to identify patients' rehabilitation state, and then design targeted individual rehabilitation plan, and improve the objectivity of rehabilitation plan design. Therefore, human pose recognition technology has become a research hotspot in the field of rehabilitation medicine. However, there are not many studies on human pose estimation methods under scene constraints. To solve this problem, a body pose estimation method based on physical constraint and physiological constraint modeling was proposed in this paper. Three main constraints were established to make full use of 3D scene information and medical information to improve the body pose estimation results. Finally, through experiments, it is proved that the method proposed in this paper can greatly improve the results of 3D human pose estimation and can better cope with joint abnormalities in the process of fitting and accelerate the speed of fitting.