| 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
WORDS: Gymnastics, jumping, three-dimensional kinematics, computer
vision.
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