ML-based electromyography signal analysis for assessing rehabilitation exercise execution quality.

Following lower limb trauma, performing orthopaedic rehabilitation exercises is a crucial factor in successful recovery. However, many patients find it difficult to execute these movements with optimal biomechanical form. To address this problem, an electromyography-based sensor system combined with machine learning is proposed. This system records and analyses exercises performed with the aim of providing real-time feedback on execution quality. To evaluate the potential of this concept, high-density electromyographic data (32 electrodes per sensor pad) were recorded from the vastus lateralis and vastus medialis muscles while performing rehabilitation exercises. Four different exercises (squat, hip abduction, leg raises and rocking) were performed, each in one optimal and three non-optimal variations, by n = 19 participants, resulting in 3,040 exercise executions and 194,560 electromyographic recordings. Analysis of this dataset demonstrated that a Support Vector Machine algorithm can be used to classify execution quality (four classes per exercise) with an average accuracy of 83.3% (± 8.8%). In addition, it was shown that, One Class Support Vector Machine trained with solely optimal executions, an unknown exercise could be identified as either optimal or non-optimal execution with an accuracy of 76.6% (± 5.9%). These results highlight the potential of this approach to evaluate exercise execution quality during rehabilitation. In the long term, this approach could provide personalised rehabilitation feedback and improve patient outcome.