School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA.
Department of Urology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Hum Factors. 2024 Apr;66(4):1081-1102. doi: 10.1177/00187208221129940. Epub 2022 Nov 11.
This study developed and evaluated a mental workload-based adaptive automation (MWL-AA) that monitors surgeon cognitive load and assist during cognitively demanding tasks and assists surgeons in robotic-assisted surgery (RAS).
The introduction of RAS makes operators overwhelmed. The need for precise, continuous assessment of human mental workload (MWL) states is important to identify when the interventions should be delivered to moderate operators' MWL.
The MWL-AA presented in this study was a semi-autonomous suction tool. The first experiment recruited ten participants to perform surgical tasks under different MWL levels. The physiological responses were captured and used to develop a real-time multi-sensing model for MWL detection. The second experiment evaluated the effectiveness of the MWL-AA, where nine brand-new surgical trainees performed the surgical task with and without the MWL-AA. Mixed effect models were used to compare task performance, objective- and subjective-measured MWL.
The proposed system predicted high MWL hemorrhage conditions with an accuracy of 77.9%. For the MWL-AA evaluation, the surgeons' gaze behaviors and brain activities suggested lower perceived MWL with MWL-AA than without. This was further supported by lower self-reported MWL and better task performance in the task condition with MWL-AA.
A MWL-AA systems can reduce surgeons' workload and improve performance in a high-stress hemorrhaging scenario. Findings highlight the potential of utilizing MWL-AA to enhance the collaboration between the autonomous system and surgeons. Developing a robust and personalized MWL-AA is the first step that can be used do develop additional use cases in future studies.
The proposed framework can be expanded and applied to more complex environments to improve human-robot collaboration.
本研究开发并评估了一种基于心理工作量的自适应自动化（MWL-AA），该系统可监测外科医生的认知负荷，并在认知要求高的任务中提供帮助，辅助外科医生进行机器人辅助手术（RAS）。
RAS 的引入使操作人员不堪重负。精确、连续评估人类心理工作量（MWL）状态的需求对于确定何时应干预以适度操作人员的 MWL 非常重要。
本研究中提出的 MWL-AA 是一种半自动抽吸工具。第一项实验招募了 10 名参与者，让他们在不同的 MWL 水平下执行手术任务。采集生理响应并用于开发实时多传感器 MWL 检测模型。第二项实验评估了 MWL-AA 的有效性，其中 9 名全新的手术学员在有和没有 MWL-AA 的情况下执行手术任务。使用混合效应模型比较任务表现、客观和主观测量的 MWL。
所提出的系统预测高 MWL 出血情况的准确率为 77.9%。对于 MWL-AA 的评估，外科医生的注视行为和大脑活动表明，使用 MWL-AA 比不使用 MWL-AA 时感知的 MWL 更低。这进一步得到了更低的自我报告 MWL 和更好的任务表现的支持，即在有 MWL-AA 的任务条件下。
MWL-AA 系统可以减轻外科医生在高压力出血情况下的工作负荷并提高手术表现。研究结果强调了在更复杂的环境中利用 MWL-AA 来增强自主系统和外科医生之间的协作的潜力。开发强大和个性化的 MWL-AA 是未来研究中开发其他用例的第一步。
所提出的框架可以扩展并应用于更复杂的环境，以提高人机协作。
