Automated evaluation of physical therapy exercises using multi-template dynamic time warping on wearable sensor signals
We use five wearable motion sensor units on the arms or legs. We acquire a physical therapy data set with 1320 exercise executions. We develop the MTMM-DTW algorithm for multiple exercise templates. The algorithm can detect, classify, and evaluate physical therapy exercises. We achieve 93.5% accuracy for exercise classification.
physical therapy dynamic time warping motion sensors inertial sensors pattern recognition motion capture