An SOM-Based Motion Trajectory Recognition Algorithm and Its Application in Monitoring Therapeutic Exercises

Summary form only given. At the rehabilitation departments of hospitals in Taiwan, it is not unusual that several patients are assigned to one exercise physiologist or physical therapist during the rehabilitation treatment at a clinical room. One-by-one clinical service is not possible; therefore, an exercise physiologist or a physical therapist cannot know how well his or her patients are practicing the assigned exercises. In addition, patients themselves usually do not have the enough knowledge about whether they are practicing correct therapeutic exercises, leading to ineffective rehabilitation and even produce adverse compensation. Therefore, to alleviate the burdens of exercise physiologists or physical therapists, a therapeutic exercise monitoring system which can automatically measure how well a patient is practicing the suggested exercises deserves to be developed. A good therapeutic exercise monitoring system should be able to provide a measurement report about how well a patient is practicing one suggested exercise; issue a warning alarm signal when an incorrect exercise is detected, etc. For this to happen, it requires an effective motion trajectory recognition algorithm which is able to evaluate whether the therapeutic exercise performed by a patient matches its corresponding assigned exercise. The automatic motion trajectory recognition turns out to be very challenging because motion trajectories are spatial-temporal patterns. There are several surveys on the topics about the domain of human motion analysis. Basically, there are three kinds of approaches (e.g., DTW, HMM, recurrent neural networks, etc.) to spatial-temporal pattern recognition. Each approach has its advantages and limitations. In this talk, a SOM-based motion trajectory recognition algorithm will be introduced. The algorithm starts with the generation of basic posture unit map. The sequence of a motion trajectory is transformed into a 2-D trajectory map based on the basic posture unit map. Then the problem of recognizing motion trajectories is transformed to the problem of recognizing 2-D trajectory maps. Finally, an unknown motion trajectory is classified to be the motion trajectory with the maximum similarity in the motion templates via a template matching technique. The performance of the proposed the SOM-based motion trajectory recognition algorithm is tested on a database consisting of 12 different types of therapeutic exercises. The average 98.8% correct rate was achieved.