Online affect detection and robot behavior adaptation for intervention of children with autism
Online affect detection and robot behavior adaptation for intervention of children with autism
复制标题
DOI：
10.1109/tro.2008.2001362
复制
发表时间：
2008-08-01
影响因子：
7.8
通讯作者：
Stone, Wendy
中科院分区：
文献类型：
--
作者：
Liu, Changchun;Conn, Karla;Stone, Wendy
近年来，对自闭症谱系障碍（ASD）儿童的机器人辅助干预的研究取得了进展。参与干预的治疗师必须克服ASD儿童通常表现出的沟通障碍，通过熟练地推断儿童的情感线索来相应地调整干预措施。同样，机器人也必须能够理解这些儿童的情感需求，目前的机器人辅助ASD干预系统缺乏的能力，以实现有效的互动，解决情感状态在人机互动和干预实践中的作用。在本文中，我们提出了一个基于生理学的影响推理机制，机器人辅助干预，机器人可以检测到的情感状态的ASD儿童识别的治疗师，并相应地调整其行为。这篇论文是开发“理解”机器人用于未来ASD干预的第一步。来自概念验证实验的六名ASD儿童的实验结果（即，基于机器人的篮球比赛）。机器人学习每个孩子对游戏配置的个人喜好程度，并选择适当的行为以他/她喜欢的程度呈现任务。实验结果表明，该机器人能够真实的实时地自动预测个人喜好程度，准确率为81.1%.据我们所知，这是第一次通过基于生理学的情感识别技术在真实的时间内检测到ASD儿童的情感状态。这也是第一次通过实验证明机器人和ASD儿童之间的情感敏感闭环互动的影响。
Investigation into robot-assisted intervention for children with autism spectrum disorder (ASD) has gained momentum in recent years. Therapists involved in interventions must overcome the communication impairments generally exhibited by children with ASD by adeptly inferring the affective cues of the children to adjust the intervention accordingly. Similarly, a robot must also be able to understand the affective needs of these children-an ability that the current robot-assisted ASD intervention systems lack-to achieve effective interaction that addresses the role of affective states in human-robot interaction and intervention practice. In this paper, we present a physiology-based affect-inference mechanism for robot-assisted intervention where the robot can detect the affective states of a child with ASD as discerned by a therapist and adapt its behaviors accordingly. This paper is the first step toward developing "understanding" robots for use in future ASD intervention. Experimental results with six children with ASD from a proof-of-concept experiment (i.e., a robot-based basketball game) are presented. The robot learned the individual liking level of each child with regard to the game configuration and selected appropriate behaviors to present the task at his/her preferred liking level. Results show that the robot automatically predicted individual liking level in real time with 81.1 % accuracy. This is the first time, to our knowledge, that the affective states of children with ASD have been detected via a physiology-based affect recognition technique in real time. This is also the first time that the impact of affect-sensitive closed-loop interaction between a robot and a child with ASD has been demonstrated experimentally.