Automated evaluation of physical therapy exercises using multi-template dynamic time warping on wearable sensor signals

This paper describes an autonomous system developed to detect and evaluate physical therapy exercises using wearable motion sensors. It is proposed that the multi-template multi-match dynamic time warping (MTMM-DTW) algorithm as a natural extension of DTW to detect multiple occurrences of more than one exercise type in the recording of a physical therapy session. While allowing some distortion (warping) in time, the algorithm provides a quantitative measure of similarity between an exercise execution and previously recorded templates, based on DTW distance. It can detect and classify the exercise types, and count and evaluate the exercises as correctly/incorrectly performed, identifying the error type, if any. To evaluate the algorithm’s performance, a data set consisting of one reference template and 10 test executions of three execution types of eight exercises performed by five subjects was recorded. Thus a total of 120 and 1200 exercise executions in the reference and test sets, respectively were recorded. The test sequences also contain idle time intervals. The accuracy of the proposed algorithm is 93.46% for exercise classification only and 88.65% for simultaneous exercise and execution type classification. The algorithm misses 8.58% of the exercise executions and demonstrates a false alarm rate of 4.91%, caused by some idle time intervals being incorrectly recognized as exercise executions. To test the robustness of the system to unknown exercises,leave-one-exercise-out cross validation was used. This results in a false alarm rate lower than 1%, demonstrating the robustness of the system to unknown movements.

The proposed system can be used for evaluating the effectiveness of a physical therapy session and for providing feedback to the patient.