Motion Estimation using a Statistical Solid Dynamic Method

The most frequently used method in three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. The measured motion is composed of the rigid bone motion and the surrounding soft tissues deformations. Soft tissue deformations introduce a significant artifact which strongly influences the bone position and orientation and joint kinematics estimation. In this study, we approached the problem of soft tissue artifacts using a statistical solid dynamics method. The statistical solid dynamics method is a combination of several previously reported tools. The first tool is called Point Cluster Technique (PCT). It is based on a least squares optimization of markers’ position and orientation. The second tool is a Kalman filter, which was added to the PCT in this study. The methods were tested and evaluated on a controlled human movement, when the subject was asked to move his arm in a simple planar motion while his arm was constrained to a designed arm handle. Eighteen subjects participated in the experiment in order to get statistically significant results. The result of these experiments indicated that adding Kalman filter to the PCT method produced a more accurate signal. However, it could not be concluded that the proposed Kalman filter is better than the low pass filter in the estimation of the human arm motion. Addition of a Kalman filter to the PCT method in the estimation procedure of rigid body motion results in a smoother signal that better represents the real motion. However, implementation of the Kalman filter with a better biomechanical motion model will probably improve the results.