CLUSTERING SURGEMES USING PROTOTYPES FROM ROBOTIC KINEMATIC INFORMATION

Abstract

Training a surgeon to be skilled and competent to perform a given surgical procedure is essential in providing a high quality of care and reducing the risk of complications. However, existing training techniques limit us from conducting in-depth analyses of surgical motions to evaluate these skills accurately. We develop a method to identify the gestures by applying unsupervised methods to cluster the surgical activities learned directly from raw kinematic data. We design an unsupervised method to determine the surgical motions in a Suturing procedure based on predefined surgical gestures. The first step is to find the prototypes by clustering the surgemes of the expert surgeon from all the same expert trials. Then, we map the other surgeons surgemes to the nearest representative of the prototypes and report the clustering accuracy by employing the rand index technique. We utilize four techniques in our proposed unsupervised approach for gesture clustering based on Hierarchical and FCM algorithms. In addition, we highlight the advantages of representing time series data before clustering in terms of computation time saving and system complexity reduction, respectively.