EMGに基づく異方性仮想拘束の適応制御によるロボット支援手術の切除・剥離支援
EMG-Based Adaptation of Anisotropic Virtual Fixtures for Robot-Assisted Surgical Resection and Dissection
手術支援ロボットにおいて、筋電位信号から術者の意図を推定し、仮想拘束の形状をリアルタイムに適応させる枠組みを提案。切除・剥離タスクで精度と動作の一貫性が向上し、認知負荷も軽減された。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Dario Onfiani, Michael Dyck, Luigi Biagiotti, Julian Klodmann
分類: cs.RO
原文アブストラクト
In this paper, we address the development of an adaptive assistance system for robot-assisted laparoscopic surgery, specifically for delicate tasks such as Resection and Dissection. Even if Virtual Fixtures offer significant advantages for guiding a surgeon's movements, conventional Virtual Fixtures are often defined by fixed geometries, lacking the flexibility to adapt to the surgical workflow or the surgeon's immediate intent. To address these limitations, we propose a novel framework for an adaptive and anisotropic virtual fixture. In addition, we introduce an intuitive control interface that modulates the fixture's geometry in real-time based on the surgeon's intent, inferred from EMG signals. This approach allows the surgeon to dynamically expand or disengage the constraint by contracting their forearm muscles, enabling seamless transitions between precise guided motion and free repositioning of the tool. Experimental results from a pilot user study, based on a standardized surgical training task, demonstrate the effectiveness of the proposed method. The system showed significant improvements in task accuracy and movement consistency, alongside a reduction in perceived cognitive load, effort, and frustration.
