EMG-Based Adaptation of Anisotropic Virtual Fixtures for Robot-Assisted Surgical Resection and Dissection

2026-06-11

AI Summary

This work proposes an anisotropic virtual fixture system for robot-assisted surgery that dynamically adapts to the surgeon’s intent and procedural context through real-time surface electromyography (EMG) control. Addressing the limitations of conventional virtual fixtures—which lack adaptability and thus constrain operational flexibility and precision—the system leverages EMG decoding to interpret forearm muscle activity, enabling continuous adjustment of constraint strength and direction. This facilitates seamless transitions between guided assistance and freehand manipulation. Integrating EMG-based intent recognition, anisotropic fixture modeling, and real-time control, the framework was validated on a laparoscopic surgical platform. User studies demonstrate significant improvements in task accuracy and movement consistency, alongside reduced cognitive load, physical effort, and frustration, thereby enhancing the naturalness and fluency of human–robot collaboration.

Abstract

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.

Problem

• Virtual Fixtures
• Robot-Assisted Surgery
• Surgical Resection
• Surgical Dissection
• Adaptive Assistance

Innovation

• EMG-based control
• adaptive virtual fixtures
• anisotropic constraints
• robot-assisted surgery
• surgical intent recognition