Comparing Feature Engineering and End-to-End Deep Learning for Autism
  Spectrum Disorder Assessment based on Fullbody-Tracking
Alberto Altozano Maria Eleonora Minissi Mariano Alcañiz Javier Marín-Morales
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. Thus, end-to-end models have
become promising methods to overcome the need for feature engineering. Our aim
is to assess both approaches across various kinematic tasks to measure the
efficacy of commonly used features in ASD assessment, while comparing them to
end-to-end models. Specifically, we developed a virtual reality environment
with multiple motor tasks and trained models using both classification
approaches. We prioritized a reliable validation framework with repeated
cross-validation. Our comparative analysis revealed that hand-crafted features
outperformed our deep learning approach in specific tasks, achieving a
state-of-the-art area under the curve (AUC) of 0.90$\pm$0.06. Conversely,
end-to-end models provided more consistent results with less variability across
all VR tasks, demonstrating domain generalization and reliability, with a
maximum task AUC of 0.89$\pm$0.06. These findings show that end-to-end models
enable less variable and context-independent ASD assessments without requiring
domain knowledge or task specificity. However, they also recognize the
effectiveness of hand-crafted features in specific task scenarios.