用深度学习区分自闭症与正常儿童的互动行为并模拟其表现
Modeling of ASD/TD Children's Behaviors in Interaction with a Virtual Social Robot During a Music Education Program Using Deep Neural Networks
- 基于动作和触碰数据，用Transformer模型区分自闭症与正常儿童
- 分类准确率81%，灵敏度达96%，可有效识别差异
- 生成的行为让专家难辨真伪，适合诊断辅助与治疗训练
本研究旨在通过深度神经网络构建智能系统，评估儿童在音乐教育项目中与虚拟社交机器人互动时的表现，并提取自闭症（ASD）与神经典型（TD）儿童的行为模型。系统具备两大功能：一是基于行为数据区分自闭症儿童与正常儿童；二是利用深度学习生成与真实情况相似的自闭症或正常儿童行为。该研究使用了伊朗沙里夫理工大学社会与认知机器人实验室先前收集的数据，包含9名自闭症儿童和21名正常儿童的有效数据。系统在结合冲击数据与运动信号的基础上，实现了81%的分类准确率与96%的敏感度。此外，设计了一种基于Transformer的网络以重现儿童行为，领域专家在区分真实与生成行为时的准确率为53.5%，一致性达68%，表明模型具有高度真实性。该系统有助于疾病诊断、治疗师培训及对障碍的理解。
原文摘要 · Abstract (English)
This research aimed to develop an intelligent system to evaluate performance and extract behavioral models for children with ASD and neurotypical (TD) children by interacting with a virtual social robot in a music education program using deep neural networks. The system has two main features: 1) it distinguishes between neurotypical children and those with ASD based on their behavior, and 2) generates behaviors resembling those of neurotypical or ASD children in similar situations using deep learning. Intelligent systems that identify complex patterns and simulate behavior can aid in diagnosis, therapist training, and understanding the disorder. Using data from a previous study at the Social and Cognitive Robotics Laboratory of Sharif University of Technology (including the usable data of 9 ASD and 21 TD participants), the system achieved an accuracy of 81% and sensitivity of 96% in distinguishing neurotypical children from those with ASD using both impact data and motion signals. A transformer-based network was designed to reproduce children's behaviors. Experts in the field struggled to differentiate real behaviors from reproduced ones, with an accuracy of 53.5% and agreement of 68%, indicating the model's success in simulating realistic behaviors.
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