Adaptive Response Technology for Autism Spectrum Disorders Intervention

描述（由申请人提供）：据估计，美国每 110 名儿童中就有 1 名患有自闭症谱系障碍 (ASD)。自闭症谱系障碍 (ASD) 的识别和有效治疗通常被视为突发公共卫生事件，每年需要花费 35-900 亿美元。旨在解决更高水平的社会和适应性障碍的传统干预措施已被证明对患有自闭症谱系障碍的学龄儿童和青少年的效果甚微，并且被认为是传统方法未能以适当的强度将干预策略与自然环境内和自然环境之间的特定技能缺陷系统地匹配的结果。在本拨款提案中，为了解决上述问题，制定了一项研究计划，将智能自适应技术应用为基于虚拟现实 (VR) 的干预方式，用于治疗自闭症谱系障碍 (ASD) 儿童和青少年的 a) 核心社会缺陷和 b) 应用自适应行为技能。该技术将具有可控难度水平的能力，可以根据参与者在执行任务时的行为参与度预测，与 VR 环境实时适应社交和自适应行为干预场景。行为参与是通过情感和注意力状态来实现的，因此，干预技术将对个体儿童参与的变化如何预测任务表现敏感。规则控制的适应将被纳入 VR 交互中，并根据此类修改如何提高性能进行明确检查。简而言之，这项技术将用于根据个人的独特情况自动调整任务特征，希望通过应用单独调制和支架式强化方式，极大地提高核心损伤领域的任务表现。本研究的具体目标是：1）完善作者在试点工作中已经开发的智能自适应响应技术，并设计VR环境中的自适应行为和社交任务； 2）根据视线信息开发基于生理学的个性化情感模型和注意力推理机制，并设计一个基于规则的监督器以允许对行为参与敏感的自适应强化策略； 3) 应用和检验预测模型和自适应技术在自闭症儿童中的有效性，以改善与基于 VR 的现实社交和自适应任务相关的特定学习。这种自适应响应技术在 ASD 干预中的成功将为系统性探索和应用自适应干预范式铺平道路，该范式旨在将个体缺陷与有针对性的干预相匹配。 公共健康相关性：该项目将应用并检查新型自适应虚拟现实（VR）技术作为患有自闭症谱系障碍（ASD）的儿童和青少年的潜在干预工具的功效。所提出的技术被设计为一个“智能”系统，可以根据生理数据、有关孩子正在看哪里的信息（例如眼球追踪）以及孩子的表现自动调整干预任务，以提高在现实世界应用的任务中的表现。人们相信，这项新技术的成功应用有可能开创个性化、有针对性的基于计算机的 ASD 干预的新时代，能够以更有效和更容易的方式解决该疾病的核心缺陷。

DESCRIPTION (provided by applicant): It is estimated that 1 in 110 children in the United States are affected by Autism Spectrum Disorders (ASD). The identification and effective treatment of ASD is often characterized as a public health emergency incurring a $35-90 billion annual cost. Traditional interventions designed to address higher level social and adaptive impairments have been demonstrated to be minimally effective for school-aged children and adolescents with ASD and are thought to be a result of a failure of traditional methodologies to systematically match intervention strategies to specific skill deficits within and across naturalistic settings in appropriately intensive dosages. In this grant proposal, in an attempt to address the above concerns, a research plan is developed to apply intelligent adaptive technology as a Virtual Reality (VR) based intervention modality for treatment of a) core social deficits and b) applied adaptive behavioral skills for children and adolescents with ASD. The technology will be applied with capacities for controllable levels of difficulty that can be adapted with the VR environment into both social and adaptive behavioral intervention scenarios in real-time based on predictions of behavioral engagement of participants while engaged with the tasks. Behavioral engagement is operationalized by affective and attentive states and as such, the intervention technology will be sensitive to how variations in an individual child's engagement predict task performance. Rule-governed adaptation will be incorporated into VR interactions and examined explicitly in terms of how such modifications improve performance. Simply, this technology will be applied to automatically adjust task characteristics based on an individual's unique profile in hopes that task performance within core domains of impairment will be greatly bolstered by application of individually modulated and scaffolded reinforcement modalities. The specific aims of this research are: 1) to refine the intelligent adaptive response technology that the authors have already developed in their pilot work, and design both adaptive behavior and social tasks in a VR environment; 2) to develop physiology-based individualized affective models and attention inference mechanisms from eye gaze information, and to design a rule-based supervisor to allow adaptive reinforcement strategies sensitive to behavioral engagement; and 3) to apply and examine the efficacy of prediction models and the adaptive technology in children with ASD relative to improving specific learning related to VR-based and realistic social and adaptive tasks. The success of such an adaptive response technology in ASD intervention will pave the way for systematic exploration and application of adaptive intervention paradigms aimed at matching individual deficit with targeted intervention. PUBLIC HEALTH RELEVANCE: The project will apply and examine the efficacy of a novel adaptive virtual reality (VR) technology as a potential intervention tool for children and adolescents with Autism Spectrum Disorders (ASD). The proposed technology is designed as an 'intelligent' system that automatically adjusts intervention tasks based on physiological data, information about where the child is looking (e.g., eye tracking), and how well the child is performing, in order to enhance performance on tasks that have real-world application. It is believed that the successful application of this new technology has the potential to usher in a new era of personalized, targeted computer-based ASD intervention capable of addressing the core deficits of the disorder in a manner that is more efficacious and accessible.