Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future
Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future
复制标题
DOI：
10.1145/3483598
复制
发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Maryam Pourebadi;L. Riek
中科院分区：
文献类型：
--
作者：
Maryam Pourebadi;L. Riek
几十年来，临床教育工作者一直使用机器人和虚拟患者模拟器系统（RPS）来帮助临床学习者（CL）获得关键技能，以帮助避免未来的患者伤害。这些系统可以模拟人类的生理特征，但是，他们有静态的脸，缺乏真实的面部线索，这限制了CL的参与和沉浸的描绘。在这篇文章中，我们提供了一个详细的审查现有的系统在使用中，以及描述的可能性，从人机交互和智能虚拟代理社区的新技术，以推动国家的艺术。我们还讨论了我们自己的工作在这一领域，包括新的方法，面部识别和合成的RPS系统，包括真实地显示患者面部线索（例如疼痛和中风）的能力。最后，我们讨论了该领域未来的研究方向。
Clinical educators have used robotic and virtual patient simulator systems (RPS) for dozens of years, to help clinical learners (CL) gain key skills to help avoid future patient harm. These systems can simulate human physiological traits; however, they have static faces and lack the realistic depiction of facial cues, which limits CL engagement and immersion. In this article, we provide a detailed review of existing systems in use, as well as describe the possibilities for new technologies from the human–robot interaction and intelligent virtual agents communities to push forward the state of the art. We also discuss our own work in this area, including new approaches for facial recognition and synthesis on RPS systems, including the ability to realistically display patient facial cues such as pain and stroke. Finally, we discuss future research directions for the field.