Evaluating Movement Quality Through Intrapersonal Synchronization

We present a method to measure intrapersonal synchronization of movement from motion capture data, and we show that our method is effective in classifying the level of skills of athletes performing karate kata. Our method is based on detecting relevant peaks of acceleration of limbs (arms and legs) and measuring their synchronization. We run a multiscale analysis, based on topological persistence, to rank the importance of peaks of acceleration. The resulting impulse signals are processed next with a multievent class synchronization algorithm, in order to define an overall synchronization index that scores the level of intrapersonal synchronization with a single scalar value. We build a basic multiclass classifier, which uses just the means of indexes computed on the different classes in the training set. We make a statistical analysis and a cross validation of the classifier on real data. Performances by athletes from three levels of skill have been recorded, classified by experts, and used to test our method. Cross validation of the classifier is performed by leave-one-out and bootstrap resampling. Results show that our method can classify correctly with very high probability (beyond $ ext99 ext%$), while it succeeds on $ ext100 ext%$ of the data used in cross validation.