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Segmenting and recognizing surgical operation trajectories into distinct,meaningful gestures is a critical preliminary step in surgical workflowanalysis for robot-assisted surgery. This step is necessary for facilitatinglearning from demonstrations for autonomous robotic surgery, evaluatingsurgical skills, and so on. In this work, we develop a hierarchicalsemi-supervised learning framework for surgical gesture segmentation usingmulti-modality data (i.e. kinematics and vision data). More specifically,surgical tasks are initially segmented based on distance characteristics-basedprofiles and variance characteristics-based profiles constructed usingkinematics data. Subsequently, a Transformer-based network with a pre-trained`ResNet-18' backbone is used to extract visual features from the surgicaloperation videos. By combining the potential segmentation points obtained fromboth modalities, we can determine the final segmentation points. Furthermore,gesture recognition can be implemented based on supervised learning. Theproposed approach has been evaluated using data from the publicly availableJIGSAWS database, including Suturing, Needle Passing, and Knot Tying tasks. Theresults reveal an average F1 score of 0.623 for segmentation and an accuracy of0.856 for recognition.

Hierarchical Semi-Supervised Learning Framework for Surgical Gesture Segmentation and Recognition Based on Multi-Modality Data