Evaluation of surgical skill using machine learning with optimal wearable sensor locations.

ABSTRACT: Evaluation of surgical skills during minimally invasive surgeries is needed when recruiting new surgeons. Although surgeons' differentiation by skill level is highly complex, performance in specific clinical tasks such as pegboard transfer and knot tying could be determined using wearable EMG and accelerometer sensors. A wireless wearable platform has made it feasible to collect movement and muscle activation signals for quick skill evaluation during surgical tasks. However, it is challenging since the placement of multiple wireless wearable sensors may interfere with their performance in the assessment. This study utilizes machine learning techniques to identify optimal muscles and features critical for accurate skill evaluation. This study enrolled a total of twenty-six surgeons of diffe

SUBMITTER: Soangra R 

PROVIDER: S-EPMC9165861 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature