GRU and LSTM Models Achieve 98.75% Accuracy in Detecting Autism Self-Stimulatory Behaviors from Video

Jul 10, 2026

Researchers trained LSTM and GRU models on pose-derived features from the SSBD dataset to classify autism-related self-stimulatory behaviors, achieving peak accuracies of 97.5% and 98.75% respectively at a sampling interval of every 15 frames. The study also evaluated ten data augmentation strategies, finding horizontal flip most effective and upsampling critical for performance.

Why it matters: This work provides concrete guidance on architecture selection, sampling rate, and augmentation for video-based behavioral classification in data-scarce clinical domains, potentially enabling scalable remote screening for autism.

Full story at: arXiv AI/ML ↗