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FECCS:用于自闭症谱系障碍中国儿童的面部情绪认知和训练系统。

FECTS: A Facial Emotion Cognition and Training System for Chinese Children with Autism Spectrum Disorder.

机构信息

Shenzhen Maternal and Child Health Hospital, Shenzhen 518000, China.

Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, China.

出版信息

Comput Intell Neurosci. 2022 Apr 27;2022:9213526. doi: 10.1155/2022/9213526. eCollection 2022.

Abstract

Traditional training methods such as card teaching, assistive technologies (e.g., augmented reality/virtual reality games and smartphone apps), DVDs, human-computer interactions, and human-robot interactions are widely applied in autistic rehabilitation training in recent years. In this article, we propose a novel framework for human-computer/robot interaction and introduce a preliminary intervention study for improving the emotion recognition of Chinese children with an autism spectrum disorder. The core of the framework is the Facial Emotion Cognition and Training System (FECTS, including six tasks to train children with ASD to match, infer, and imitate the facial expressions of happiness, sadness, fear, and anger) based on Simon Baron-Cohen's E-S (empathizing-systemizing) theory. Our system may be implemented on PCs, smartphones, mobile devices such as PADs, and robots. The training record (e.g., a tracked record of emotion imitation) of the Chinese autistic children interacting with the device implemented using our FECTS will be uploaded and stored in the database of a cloud-based evaluation system. Therapists and parents can access the analysis of the emotion learning progress of these autistic children using the cloud-based evaluation system. Deep-learning algorithms of facial expressions recognition and attention analysis will be deployed in the back end (e.g., devices such as a PC, a robotic system, or a cloud system) implementing our FECTS, which can perform real-time tracking of the imitation quality and attention of the autistic children during the expression imitation phase. In this preliminary clinical study, a total of 10 Chinese autistic children aged 3-8 are recruited, and each of them received a single 20-minute training session every day for four consecutive days. Our preliminary results validated the feasibility of the developed FECTS and the effectiveness of our algorithms based on Chinese children with an autism spectrum disorder. To verify that our FECTS can be further adapted to children from other countries, children with different cultural/sociological/linguistic contexts should be recruited in future studies.

摘要

传统的训练方法,如卡片教学、辅助技术(例如增强现实/虚拟现实游戏和智能手机应用程序)、DVD、人机交互和人机机器人交互,近年来在自闭症康复训练中得到了广泛应用。在本文中,我们提出了一种新的人机/机器人交互框架,并介绍了一项初步的干预研究,旨在提高中国自闭症谱系障碍儿童的情绪识别能力。该框架的核心是基于 Simon Baron-Cohen 的 E-S(共情-系统化)理论的面部情绪认知和训练系统(FECTS,包括六个任务,旨在训练 ASD 儿童匹配、推断和模仿快乐、悲伤、恐惧和愤怒的面部表情)。我们的系统可以在 PC、智能手机、PAD 等移动设备和机器人上实现。中国自闭症儿童与我们的 FECTS 设备交互的训练记录(例如,情绪模仿的跟踪记录)将被上传并存储在基于云的评估系统的数据库中。治疗师和家长可以使用基于云的评估系统访问这些自闭症儿童情绪学习进展的分析。面部表情识别和注意力分析的深度学习算法将部署在后端(例如,PC、机器人系统或云系统等设备)中,实现我们的 FECTS,可以在表情模仿阶段实时跟踪自闭症儿童的模仿质量和注意力。在这项初步的临床研究中,共招募了 10 名年龄在 3-8 岁的中国自闭症儿童,他们每人每天接受一次 20 分钟的训练,连续进行四天。我们的初步结果验证了开发的 FECTS 的可行性和基于中国自闭症儿童的算法的有效性。为了验证我们的 FECTS 可以进一步适用于来自其他国家的儿童,未来的研究应该招募来自不同文化/社会学/语言学背景的儿童。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bae6/9068315/18e6f767b459/CIN2022-9213526.001.jpg

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