Detection and Evaluation of Physical Therapy Exercises by Dynamic Time Warping Using Wearable Motion Sensor Units

We develop an autonomous system that detects and evaluates physical therapy exercises using wearable motion sensors. We propose an algorithm that detects all the occurrences of one or more template signals (representing exercise movements) in a long signal acquired during a physical therapy session. In matching the signals, the algorithm allows some distortion in time, based on dynamic time warping (DTW). The algorithm classifies the executions in one of the exercises and evaluates them as correct/incorrect, giving the error type if there is any. It also provides a quantitative measure of similarity between each matched execution and its template. To evaluate the performance of the algorithm in physical therapy, a dataset consisting of one template execution and ten test executions of each of the three execution types of eight exercises performed by five subjects is recorded, having a total of 120 and 1,200 exercise executions in the training and test sets, respectively, as well as many idle time intervals in the test signals. The proposed algorithm detects 1,125 executions in the whole test set. 8.58 % of the 1,200 executions are missed and 4.91 % of the idle time intervals are incorrectly detected as executions. The accuracy is 93.46 % only for exercise classification and 88.65 % for simultaneous exercise and execution type classification. The proposed system may be used for both estimating the intensity of the physical therapy session and evaluating the executions to provide feedback to the patient and the specialist.