  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.
