基于EMG的各向异性虚拟夹具自适应方法用于机器人辅助手术切除与解剖
EMG-Based Adaptation of Anisotropic Virtual Fixtures for Robot-Assisted Surgical Resection and Dissection
AI总结：
提出一种基于EMG信号自适应调节各向异性虚拟夹具的框架，通过实时推断外科医生意图动态调整约束，实验证明能提高手术精度和运动一致性，降低认知负荷。
摘要：
本文针对机器人辅助腹腔镜手术中的精细任务（如切除和解剖），开发了一种自适应辅助系统。尽管虚拟夹具在引导外科医生运动方面具有显著优势，但传统虚拟夹具通常由固定几何形状定义，缺乏适应手术流程或外科医生即时意图的灵活性。为解决这些局限性，我们提出了一种自适应各向异性虚拟夹具的新框架。此外，我们引入了一种直观的控制接口，该接口基于从EMG信号推断的外科医生意图，实时调节夹具的几何形状。该方法允许外科医生通过收缩前臂肌肉动态扩展或解除约束，实现精确引导运动和工具自由重新定位之间的无缝切换。基于标准化手术训练任务的初步用户研究实验结果表明了所提方法的有效性。该系统在任务精度和运动一致性方面表现出显著改善，同时降低了感知认知负荷、努力和挫败感。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
