Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder
Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder
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DOI：
10.1016/j.ijhcs.2008.04.003
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发表时间：
2008-09-01
影响因子：
5.4
通讯作者：
Stone, Wendy
中科院分区：
文献类型：
--
作者：
Liu, Changchun;Conn, Karla;Stone, Wendy
一般来说，经验丰富的治疗师会持续监测自闭症谱系障碍（ASD）儿童的情感线索，并相应地调整干预的过程。在这项工作中，我们通过设计基于ASD儿童生理反应的治疗师式情感模型来解决如何使基于计算机的ASD干预工具具有影响敏感性的问题。设计了两个基于计算机的认知任务，以引出喜欢、焦虑和参与的情感状态，这在自闭症干预中被认为是重要的。大量的生理指标可能与ASD儿童的上述情感状态相关。为了将生理数据与情感状态联系起来，有可靠的参考点，我们收集和分析了来自治疗师、家长和儿童自己的情感状态主观报告。当使用治疗师的报告时，基于支持向量机（SVM）的情感模型产生可靠的预测，成功率约为82.9%。据我们所知，这是第一次通过基于生理学的情感识别技术实验检测自闭症儿童的情感状态。(C) 2008 Elsevier Ltd版权所有。
Generally, an experienced therapist continuously monitors the affective cues of the children with Autism Spectrum Disorders (ASD) and adjusts the course of the intervention accordingly. In this work, we address the problem of how to make the computer-based ASD intervention tools affect-sensitive by designing therapist-like affective models of the children with ASD based on their physiological responses. Two computer-based cognitive tasks are designed to elicit the affective states of liking, anxiety, and engagement that are considered important in autism intervention. A large set of physiological indices are investigated that may correlate with the above affective states of children with ASD. In order to have reliable reference points to link the physiological data to the affective states, the subjective reports of the affective states from a therapist, a parent, and the child himself/herself were collected and analyzed. A support vector machines (SVM)-based affective model yields reliable prediction with approximately 82.9% success when using the therapist's reports. This is the first time, to our knowledge, that the affective states of children with ASD have been experimentally detected via physiology-based affect recognition technique. (C) 2008 Elsevier Ltd. All rights reserved.