Maximizing height, distance or rotation from real-time analysis visualisation of take-off angles and speed

Studies to optimise take off angles for height or distance have usually involved either a time-consuming invasive approach of placing markers on the body in a laboratory setting or using even less efficient manual frame-by-frame joint angle calculations with one of the many sport science video analysis software tools available. This research introduces a computer-vision based, marker-free, real-time biomechanical analysis approach to optimise take-off angles based on speed, base of support and dynamically calculated joint angles and mass of body segments. The goal of a jump is usually for height, distance or rotation with consequent dependencies on speed and phase of joint angles, centre of mass COM) and base of support. First and second derivatives of joint angles and body part COMs are derived from a Continuous Human Movement Recognition (CHMR) system for kinematical and what-if calculations. Motion is automatically segmented using hierarchical Hidden Markov Models and 3D tracking is further stabilized by estimating the joint angles for the next frame using a forward smoothing Particle filter. The results from a study of jumps, leaps and summersaults supporting regular knowledge of results feedback during training sessions indicate that this approach is useful for optimising the height, distance or rotation of skills. Key PointsComputer-vision based marker-free tracking.Real-time biomechanical analysis.Improve tracking using a forward smoothing Particle filter.Automatically segment using hierarchical Hidden Markov Models.Recognize skills using segmented motion.Optimize take-off angles using speed, base of support, joint angles and mass of body segments.Optimize height, distance or rotation of skills.