RoboPatient - Robot assisted learning of constrained haptic information gain

医生对病人的初步检查通常包括体格检查，以估计腹部内脏器官的状况。在医生中，将触觉线索与视觉反应反馈相结合的技能是建立信任和提高初步诊断准确性的关键，这是一种隐性知识。医学培训的临床课程包括使用真实病人或静态橡胶人体模型。这两种方法对一些关键信息都是不透明的，比如医生手指硬度的变化如何影响器官边界的压力水平。此外，在病房演示中给出的反馈通常是定性的。该项目的目标是研究功能性机器人患者如何在专家演示者和实习医生之间建立有效的联系，以发展两名人类同行无法在真实患者身上建立的触诊技能。在这个项目中，我们介绍了第一代具有传感和可控内部器官的功能性机器人患者，以及详细的基于有限元的腹部组织模型，以可视化增强的传感器数据。在机器人病人的身体检查过程中，使用基于增强现实的面部反应，将丰富人机交互。这将有助于实习医生从导师那里获得更深入的见解。机器人病人还可以让我们量化和加强现有的技术——例如，从遥远的区域开始“校准”外围感觉系统，同时学习代表最佳方法的方法——例如，以适当的频率按压足够的地方。因此，经验反馈循环将允许我们量化改进。该项目的主要科学成果将是更深入地了解专家医生如何使用机器人辅助方法，让实习医生在视觉感知限制的情况下有效地获得手动触诊技能。第一次试验将在萨里大学进行，随后通过皇家全科医生学院的讲习班向其他全科医生培训师和学员介绍。来自试点试验的学生和导师反馈将用于在以用户为中心的协同设计框架中改进机器人-患者设计。

Primary examination of a patient by a physician often involves physical examination to estimate the condition of internal organs in the abdomen. It is tacit knowledge among physicians that the skill to combine haptic cues with visual reaction feedback during physical examination of a patient is key to establish trust and to improve the accuracy of preliminary diagnosis. Clinical sessions in medical training involve either using real patients or static rubber mannequins. Both methods are opaque to some key information - such as how variation of physician's finger stiffness affects stress levels at organ boundaries. Moreover, feedback given in ward demonstrations is often qualitative. The ambition of this project is to investigate how a functional robotic patient can establish an efficient link between an expert demonstrator and a trainee physician to develop palpation skills that the two human counterparts cannot establish using a real patient. In this project, we introduce the first generation of a functional robotic patient with sensorised and controllable internal organs together with a detailed finite element-based abdominal tissue model to visualise enhanced sensor data. The human-robot interaction will be enriched using augmented reality based facial reactions during physical examination of the robotic patient. This will help trainee physicians to derive deeper insights into the demonstrations from a tutor. A robotic patient would also allow us to quantify and reinforce established techniques - for example starting distant regions to "calibrate" the peripheral sensory system, while learning what represents optimal approaches - such as pressing just enough with the right frequency. The experiential feedback loop will, therefore, allow us to quantify the improvement.The main scientific outcome of this project will be a deeper understanding of how an expert physician could use a robot-assisted approach to allow a trainee physician to efficiently acquire the skill of manual palpation subject to visually perceived constraints. First trials will be done at the University of Surrey, with a subsequent introduction to other General Practitioner trainers and trainees through Royal College of General Practitioners workshops. Student and tutor feedback from pilot trials will be used to improve the robo-patient design in a user-centred co-design framework.