Protpagorn Namnueng, Lalitharatne Thilina Dulantha, Costi Leone, Iida Fumiya
Bio Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
Front Robot AI. 2023 Sep 13;10:1122914. doi: 10.3389/frobt.2023.1122914. eCollection 2023.
Abdominal palpation is one of the basic but important physical examination methods used by physicians. Visual, auditory, and haptic feedback from the patients are known to be the main sources of feedback they use in the diagnosis. However, learning to interpret this feedback and making accurate diagnosis require several years of training. Many abdominal palpation training simulators have been proposed to date, but very limited attempts have been reported in integrating vocal pain expressions into physical abdominal palpation simulators. Here, we present a vocal pain expression augmentation for a robopatient. The proposed robopatient is capable of providing real-time facial and vocal pain expressions based on the exerted palpation force and position on the abdominal phantom of the robopatient. A pilot study is conducted to test the proposed system, and we show the potential of integrating vocal pain expressions to the robopatient. The platform has also been tested by two clinical experts with prior experience in abdominal palpation. Their evaluations on functionality and suggestions for improvements are presented. We highlight the advantages of the proposed robopatient with real-time vocal and facial pain expressions as a controllable simulator platform for abdominal palpation training studies. Finally, we discuss the limitations of the proposed approach and suggest several future directions for improvements.
腹部触诊是医生使用的基本但重要的体格检查方法之一。来自患者的视觉、听觉和触觉反馈是他们在诊断中使用的主要反馈来源。然而,学会解读这种反馈并做出准确诊断需要数年的培训。迄今为止,已经提出了许多腹部触诊训练模拟器,但将声音疼痛表达整合到物理腹部触诊模拟器中的尝试报道非常有限。在此,我们提出了一种用于机器人患者的声音疼痛表达增强方法。所提出的机器人患者能够根据施加在机器人患者腹部模型上的触诊力和位置提供实时面部和声音疼痛表达。进行了一项初步研究来测试所提出的系统,并且我们展示了将声音疼痛表达整合到机器人患者中的潜力。该平台还由两位有腹部触诊经验的临床专家进行了测试。展示了他们对功能的评估以及改进建议。我们强调了所提出的具有实时声音和面部疼痛表达的机器人患者作为腹部触诊训练研究的可控模拟器平台的优势。最后,我们讨论了所提出方法的局限性,并提出了几个未来改进的方向。