Autism Spectrum Disorder (ASD) is characterized by challenges in social communication and restricted patterns, with motor abnormalities gaining traction for early detection. However, kinematic analysis in ASD is limited, often lacking robust validation and relying on hand-crafted features for single tasks, leading to inconsistencies across studies. End-to-end models have emerged as promising methods to overcome the need for feature engineering. Our aim is to propose a newly adapted 3DCNN ResNet from and compare it to widely used hand-crafted features for motor ASD assessment. Specifically, we developed a virtual reality environment with multiple motor tasks and trained models using both approaches. We prioritized a reliable validation framework with repeated cross-validation. Results show the proposed model achieves a maximum accuracy of 85$\pm$3%, outperforming state-of-the-art end-to-end models with short 1-to-3 minute samples. Our comparative analysis with hand-crafted features shows feature-engineered models outperformed our end-to-end model in certain tasks. However, our end-to-end model achieved a higher mean AUC of 0.80$\pm$0.03. Additionally, statistical differences were found in model variance, with our end-to-end model providing more consistent results with less variability across all VR tasks, demonstrating domain generalization and reliability. These findings show that end-to-end models enable less variable and context-independent ASD classification without requiring domain knowledge or task specificity. However, they also recognize the effectiveness of hand-crafted features in specific task scenarios.

翻译：自闭症谱系障碍（ASD）的特征在于社交沟通障碍和受限的行为模式，其中运动异常已成为早期检测的重要研究方向。然而，ASD的运动学分析研究有限，常缺乏稳健的验证，并依赖针对单一任务的手工特征提取，导致不同研究间结果不一致。端到端模型作为克服特征工程需求的有前景方法应运而生。本研究旨在提出一种新改进的3DCNN ResNet模型，并将其与广泛使用的ASD运动评估手工特征提取方法进行比较。具体而言，我们开发了包含多种运动任务的虚拟现实环境，并采用两种方法训练模型。我们优先构建了包含重复交叉验证的可靠验证框架。结果表明，所提模型在1-3分钟短样本上实现了85$\pm$3%的最高准确率，优于当前最先进的端到端模型。与手工特征提取方法的对比分析显示，特征工程模型在特定任务中表现优于我们的端到端模型。然而，我们的端到端模型取得了0.80$\pm$0.03的更高平均AUC值。此外，模型方差存在统计学差异：我们的端到端模型在所有VR任务中表现出更小的变异性，提供了更一致的结果，体现了领域泛化能力和可靠性。这些发现表明，端到端模型能够在不依赖领域知识或任务特异性的情况下，实现变异更小且与上下文无关的ASD分类。但研究也确认了手工特征提取方法在特定任务场景中的有效性。