Research indicates that human–robot interaction can help children with autism spectrum disorder (ASD). While most early robot-mediated interaction studies were based on free interactions, recent studies have shown that robot-mediated interventions that focus on the core impairments of ASD such as joint attention deficit tend to produce better outcomes. Joint attention impairment is one of the core deficits in ASD that has an important impact in the neuropsychological development of these children. In this paper, we propose a novel joint attention intervention system for children with ASD that overcomes several existing limitations in this domain such as the need to use body-worn sensors, nonautonomous robot operation requiring human involvement and lack of a formal model for robot-mediated joint attention interaction. We present a fully autonomous robotic system, called noncontact-responsive robot-mediated intervention system, that can infer attention through a distributed noncontact gaze inference mechanism with an embedded least-to-most (LTM) robot-mediated interaction model to address the current limitations. The system was tested in a multisession user study with 14 young children with ASD. The results showed that participants’ joint attention skills improved significantly, their interest in the robot remained consistent throughout the sessions, and the LTM interaction model was effective in promoting the children's performance.

中文翻译：

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针对自闭症谱系障碍幼儿的无创自主机器人介导的联合注意力干预系统的设计、开发和评估


研究表明，人机交互可以帮助患有自闭症谱系障碍 (ASD) 的儿童。虽然大多数早期的机器人介导的交互研究都是基于自由交互，但最近的研究表明，专注于自闭症谱系障碍核心损伤（例如联合注意力缺陷）的机器人介导的干预措施往往会产生更好的结果。联合注意力障碍是自闭症谱系障碍的核心缺陷之一，对这些儿童的神经心理发展具有重要影响。在本文中，我们提出了一种针对自闭症儿童的新型联合注意力干预系统，该系统克服了该领域现有的一些限制，例如需要使用身体佩戴的传感器、需要人类参与的非自主机器人操作以及缺乏正式的机器人模型。介导的共同注意互动。我们提出了一种完全自主的机器人系统，称为非接触响应式机器人介导的干预系统，它可以通过分布式非接触式凝视推理机制和嵌入式最小到最（LTM）机器人介导的交互模型来推断注意力，以解决当前的局限性。该系统在一项针对 14 名患有自闭症谱系障碍 (ASD) 幼儿的多会话用户研究中进行了测试。结果显示，参与者的共同注意力技能显着提高，他们对机器人的兴趣在整个课程中保持一致，LTM交互模型有效地促进了孩子们的表现。