Ershad Marzieh, Rege Robert, Majewicz Fey Ann
Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX, 75080.
Department of Surgery at UT Southwestern Medical Center, Dallas, TX, 75390.
IEEE Trans Med Robot Bionics. 2021 Nov;3(4):959-969. doi: 10.1109/tmrb.2021.3124128. Epub 2021 Oct 29.
Surgical skill directly affects surgical procedure outcomes; thus, effective training is needed to ensure satisfactory results. Many objective assessment metrics have been developed that provide the trainee with descriptive feedback about their performance however, often lack feedback on how to improve performance. The most effective training method is one that is intuitive, easy to understand, personalized to the user,and provided in a timely manner. We propose a framework to enable user-adaptive training using near real-time detection of performance, based on intuitive styles of surgical movements, and design a haptic feedback framework to assist with correcting styles of movement. We evaluate the ability of three types of force feedback (spring, damping, and spring plus damping feedback), computed based on prior user positions, to improve different stylistic behaviors of the user during kinematically constrained reaching movement tasks. The results indicate that five out of six styles studied here were improved using at least one of the three types of force feedback. Task performance metrics were compared in the presence of the three types of feedback. Task time was statistically significantly lower when applying spring feedback, compared to the other two types of feedback. Path straightness and targeting error were statistically significantly improved when using spring-damping feedback compared to the other two types of feedback. This study presents a groundwork for adaptive training in robotic surgery based on near real-time human-centric models of surgical behavior.
手术技能直接影响手术结果；因此，需要有效的培训以确保获得满意的效果。已经开发出许多客观评估指标，这些指标能为受训者提供关于其表现的描述性反馈，然而，往往缺乏关于如何提高表现的反馈。最有效的培训方法是直观、易于理解、针对用户个性化且能及时提供的方法。我们提出一个框架，基于直观的手术动作风格，利用对表现的近实时检测实现用户自适应培训，并设计一个触觉反馈框架来协助纠正动作风格。我们评估了基于先前用户位置计算的三种力反馈（弹簧、阻尼以及弹簧加阻尼反馈）在运动学约束的伸手动作任务中改善用户不同风格行为的能力。结果表明，这里研究的六种风格中有五种通过使用三种力反馈中的至少一种得到了改善。在三种反馈存在的情况下比较了任务表现指标。与其他两种反馈相比，应用弹簧反馈时任务时间在统计学上显著更低。与其他两种反馈相比，使用弹簧 - 阻尼反馈时路径直线度和目标误差在统计学上显著改善。本研究为基于近实时以人类为中心的手术行为模型的机器人手术自适应培训奠定了基础。
