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Machine Learning from Inertial Sensing Data for assess Human Motor Control Symmetry in Injury: applications for fatigue assessment pre-and-post chemotherapy
A series of objective methods will be discussed for representing changes in human motion during injury rehabilitation using Micro-Electro-Mechanical Systems (MEMS) inertial sensors. Tracking the changes over a recovery period requires methods for evaluating the similarity of movement in an impaired state against a non-impaired state. We investigate the use of motion analyses such as the center of mass (COM) tipping distance, the variance of joint velocity eigenvalues and the cumulative state changes of Gaussian mixture models (GMM) for monitoring the symmetry between the left and right sides of body during rehabilitation exercises. The methods are tested on an injured athlete over 4 months of recovery from an ankle operation and validated by comparing the observed improvement to the variation among a group of uninjured subjects. The results indicate that gradual changes are detected in the motion symmetry, thus providing quantitative measures to aid clinical decisions.