Automated Segmentation of Surgical Motion for Performance Analysis and Feedback

Advances in technology have motivated the increasing use of virtual reality simulation-based training systems in surgical education, as well as the use of motion capture systems to record surgical performance. These systems have the ability to collect large volumes of trajectory data. The capability to analyse motion data in a meaningful manner is valuable in characterising and evaluating the quality of surgical technique, and in facilitating the development of intelligent self-guided training systems with automated performance feedback. To this end, we propose an automatic trajectory segmentation technique, which divides surgical tool trajectories into their component movements according to spatio-temporal features. We evaluate this technique on two different temporal bone surgery tasks requiring the use of distinct surgical techniques and show that the proposed approach achieves higher accuracy compared to an existing method.