EECS Department, Vanderbilt University, USA.
IEEE Trans Vis Comput Graph. 2013 Apr;19(4):711-20. doi: 10.1109/TVCG.2013.42.
Autism Spectrum Disorders (ASD) are characterized by atypical patterns of behaviors and impairments in social communication. Among the fundamental social impairments in the ASD population are challenges in appropriately recognizing and responding to facial expressions. Traditional intervention approaches often require intensive support and well-trained therapists to address core deficits, with many with ASD having tremendous difficulty accessing such care due to lack of available trained therapists as well as intervention costs. As a result, emerging technology such as virtual reality (VR) has the potential to offer useful technology-enabled intervention systems. In this paper, an innovative VR-based facial emotional expression presentation system was developed that allows monitoring of eye gaze and physiological signals related to emotion identification to explore new efficient therapeutic paradigms. A usability study of this new system involving ten adolescents with ASD and ten typically developing adolescents as a control group was performed. The eye tracking and physiological data were analyzed to determine intragroup and intergroup variations of gaze and physiological patterns. Performance data, eye tracking indices and physiological features indicated that there were differences in the way adolescents with ASD process and recognize emotional faces compared to their typically developing peers. These results will be used in the future for an online adaptive VR-based multimodal social interaction system to improve emotion recognition abilities of individuals with ASD.
自闭症谱系障碍(ASD)的特征是行为模式异常和社交沟通障碍。在 ASD 人群中,基本的社交障碍包括难以正确识别和回应面部表情。传统的干预方法通常需要密集的支持和训练有素的治疗师来解决核心缺陷,而许多 ASD 患者由于缺乏可用的训练有素的治疗师以及干预成本,难以获得这种护理。因此,新兴技术,如虚拟现实(VR),有可能提供有用的技术支持的干预系统。在本文中,开发了一种创新的基于 VR 的面部情感表达呈现系统,该系统可以监测与情绪识别相关的眼动和生理信号,以探索新的有效治疗范式。对包括 10 名 ASD 青少年和 10 名典型发展青少年在内的 20 名参与者进行了这项新系统的可用性研究。分析眼动追踪和生理数据,以确定组内和组间的注视和生理模式变化。表现数据、眼动追踪指标和生理特征表明,与他们的典型发展同龄人相比,ASD 青少年处理和识别情绪面孔的方式存在差异。这些结果将用于未来的在线自适应基于 VR 的多模态社交互动系统,以提高 ASD 个体的情绪识别能力。