Unraveling the Connection: How Cognitive Workload Shapes Intent Recognition in Robot-Assisted Surgery

2025-08-03

AI Summary

To address the degradation of surgeon intent recognition accuracy under high cognitive load in robot-assisted surgery, this study proposes the first multimodal semantic understanding framework that jointly models cognitive load assessment and intent recognition. By synchronously acquiring electroencephalography (EEG), heart rate variability (HRV), electromyography (EMG), and eye-tracking signals, we develop a joint modeling approach for real-time, co-decoding of surgical intent and cognitive state. Our key innovation lies in semantically mapping physiological signals to surgical action intents and dynamically adapting decoding strategies under high cognitive load. Experimental results demonstrate that the proposed system significantly outperforms unimodal baselines in intent recognition accuracy—achieving a 12.7% improvement. Furthermore, it enables personalized surgical training feedback and human–robot collaboration optimization. This work establishes a novel, interpretable, and adaptive paradigm for intent understanding in intelligent surgical robotics.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Applications
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• How cognitive workload affects intent recognition in robot-assisted surgery
• Developing an adaptive system to monitor surgeon mental state
• Improving intent recognition using multi-modal physiological data

Innovation

• Multi-modal monitoring of cognitive workload
• Semantic understanding of surgeon intents
• Integration of brain, heart, muscle, eye data