Motion Evaluation of Therapy Exercises by Means of Skeleton Normalisation, Incremental Dynamic Time Warping and Machine Learning: A Comparison of a Rule-Based and a Machine-Learning-Based Approach

The assessment of motions by means of technical assistance systems is attracting widespread interest in fields such as competitive sports, fitness and rehabilitation. Current research has achieved to generate feedback that concerns quantity or the grade of similarity with regard to correct reference motions. In view of post-operative rehabilitation exercises, such type of feedback is regarded as insufficient. That is why recent research aims at providing a qualitative feedback by communicating motion errors. While existing systems investigated the use of manually defined rules to detect motion errors, we suggest to employ machine learning techniques in combination with dynamic time warping and to train classifiers with sample exercise executions represented by 3-D skeletons joint trajectories. This study describes both a rule-based and a machine-learning-based approach and compares them with regard to their accuracy. In the second place, this study seeks to investigate the effect of using normalised hierarchical coordinates on the classification accuracy if data of different persons is used for the machine-learning-based approach. The results reveal that the performance of the machine-learning-based method compares well with the rule-based concept. Another outcome to emerge from this study is that normalised hierarchical coordinates allow to use data of different persons.