Résumé
The advent of robotic surgery has brought about a paradigm shift in the medical field, necessitating the development of corresponding surgical skills training and assessment methods. These methods aim to enable surgeons to acquire the requisite skills for robotic surgery in the most efficient manner. Despite the progression from a master-apprentice system to manual objective assessment and then automated performance assessment methods, certain limitations have been observed. Our research aims to address these limitations by exploring muscle activity and state information during training via surface electromyography (sEMG) signals. This approach is intended to eventually provide interpretable information that can enhance the trainee's understanding of assessment feedback and facilitate skill improvement. Building on our first study that validated the feasibility of motion primitive recognition based on sEMG signals, this work compares the performance of various machine learning (ML) methods for motion primitive recognition. It also investigates the effect of different parameters of the sliding window on recognition accuracy. Our findings indicate that the deep neural network (DNN) when paired with optimal sliding window parameters, can achieve the best average accuracy of 61.76% in this study. The discoveries also provide a reference of parameter settings for variable-length sliding window approach and ML methods in recognition of robotic surgery motion based on sEMG data. By demonstrating the feasibility and exploring the most effective analysis method, this work lays down the first stone to address the research topic of integrating muscle information into multimodal surgical skill training and assessment.