Osaka Kyoko, Matsumoto Kazuyuki, Akiyama Toshiya, Tanioka Ryuichi, Betriana Feni, Zhao Yueren, Kai Yoshihiro, Miyagawa Misao, Tanioka Tetsuya, Locsin Rozzano C
Department of Psychiatric Nursing, Nursing Course of Kochi Medical School, Kochi University, Kochi 783-8505, Japan.
Graduate School of Engineering, Tokushima University, Tokushima 770-8506, Japan.
Healthcare (Basel). 2022 May 5;10(5):848. doi: 10.3390/healthcare10050848.
Rapid progress in humanoid robot investigations offers possibilities for improving the competencies of people with social disorders, although this improvement of humanoid robots remains unexplored for schizophrenic people. Methods for creating future multimodal emotional data for robot interactions were studied in this case study of a 40-year-old male patient with disorganized schizophrenia without comorbidities. The qualitative data included heart rate variability (HRV), video-audio recordings, and field notes. HRV, Haar cascade classifier (HCC), and Empath API were evaluated during conversations between the patient and robot. Two expert nurses and one psychiatrist evaluated facial expressions. The research hypothesis questioned whether HRV, HCC, and Empath API are useful for creating future multimodal emotional data about robot-patient interactions. The HRV analysis showed persistent sympathetic dominance, matching the human-robot conversational situation. The result of HCC was in agreement with that of human observation, in the case of rough consensus. In the case of observed results disagreed upon by experts, the HCC result was also different. However, emotional assessments by experts using Empath API were also found to be inconsistent. We believe that with further investigation, a clearer identification of methods for multimodal emotional data for robot interactions can be achieved for patients with schizophrenia.
人形机器人研究的快速进展为提高患有社交障碍的人的能力提供了可能性，尽管对于精神分裂症患者而言，人形机器人在这方面的改善仍未得到探索。在这项针对一名40岁、患有紊乱型精神分裂症且无合并症的男性患者的案例研究中，研究了为机器人交互创建未来多模态情感数据的方法。定性数据包括心率变异性（HRV）、视频音频记录和现场笔记。在患者与机器人对话期间，对HRV、哈尔级联分类器（HCC）和共情应用程序编程接口（Empath API）进行了评估。两名专家护士和一名精神科医生对面部表情进行了评估。研究假设质疑HRV、HCC和Empath API是否有助于创建关于机器人与患者交互的未来多模态情感数据。HRV分析显示持续的交感神经优势，这与人机对话情况相符。在大致达成共识的情况下，HCC的结果与人类观察结果一致。在专家观察结果不一致的情况下，HCC的结果也不同。然而，专家使用Empath API进行的情感评估也被发现不一致。我们相信，通过进一步研究，可以为精神分裂症患者更清晰地确定用于机器人交互的多模态情感数据的方法。
