通过多模态生理信号提升手术机器人对医生意图的识别能力
Unraveling the Connection: How Cognitive Workload Shapes Intent Recognition in Robot-Assisted Surgery
- 融合脑电、心率等多模态数据监测认知负荷
- 在高压力场景下仍能准确识别手术意图
- 适合手术训练系统与智能辅助机器人研发者
机器人辅助手术通过提高精度、减少创伤和改善患者预后，已深刻改变医疗行业。然而，手术成功高度依赖机器人系统对术者（尤其是培训中的外科医生）意图的准确理解。一个关键影响因素是术中认知负荷。本研究致力于构建一个智能自适应系统，通过多模态生理信号（脑活动、心率、肌电、眼动）实时监测认知负荷，实现对术者意图的语义级理解。该系统可增强高压力情境下的意图识别能力，从而提升手术机器人的响应准确性，进一步优化手术效果与学习成效。
原文摘要 · Abstract (English)
Robot-assisted surgery has revolutionized the healthcare industry by providing surgeons with greater precision, reducing invasiveness, and improving patient outcomes. However, the success of these surgeries depends heavily on the robotic system ability to accurately interpret the intentions of the surgical trainee or even surgeons. One critical factor impacting intent recognition is the cognitive workload experienced during the procedure. In our recent research project, we are building an intelligent adaptive system to monitor cognitive workload and improve learning outcomes in robot-assisted surgery. The project will focus on achieving a semantic understanding of surgeon intents and monitoring their mental state through an intelligent multi-modal assistive framework. This system will utilize brain activity, heart rate, muscle activity, and eye tracking to enhance intent recognition, even in mentally demanding situations. By improving the robotic system ability to interpret the surgeons intentions, we can further enhance the benefits of robot-assisted surgery and improve surgery outcomes.
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