A model-based image-matching technique for three-dimensional reconstruction of human motion from uncalibrated video sequences.

In many situations, e.g. sports injuries, three-dimensional kinematics cannot be obtained with traditional lab methods. However, if methods for reconstructing motion patterns from video sequences were available, our understanding of injury mechanisms could be improved. The aim of this study was to assess the accuracy of a new model-based image-matching technique for human motion reconstruction from one or more uncalibrated video sequences, using traditional motion analysis as a gold standard. A laboratory trial was conducted with one test subject performing jogging and side step cutting, while being filmed with three ordinary video cameras. This provided three single camera matchings, three double camera matchings and one triple camera matching for each of the motions. The test subject wore 33 reflective skin markers and was filmed with a seven-camera, 240 Hz motion analysis system. Root mean square (RMS) hip and knee flexion/extension angle differences were less than 12 degrees for all the matchings. Estimates for ad-/abduction (<15 degrees) and internal/external rotation (<16 degrees) were less precise. RMS velocity differences up to 0.62 m/s were found for the single camera matchings, but for the triple camera matching the RMS differences were less than 0.13 m/s for each direction. In conclusion, a new model-based image-matching technique has been developed, that can be used to estimate temporal joint angle histories, velocities and accelerations from uncalibrated video recordings. The kinematic estimates, in particular for center of mass velocity and acceleration, are clearly better when two or more camera views are available. This method can potentially be used to arrive at more precise descriptions of the mechanisms of sports injuries than what has been possible without elaborate methods for three-dimensional reconstruction from uncalibrated video sequences, e.g. for knee injuries.