Temporal Anomaly Detection in ADHD Using Recurrent Neural Networks

This study applies machine learning techniques (isolation forest and RNN models) to detect and predict anomalies in motor activity patterns from individuals with ADHD using the HYPERAKTIV dataset. The RNN achieved high predictive accuracy (0.953) and shows promise for identifying objective movement-related ADHD signatures to support personalized interventions and clinical decision-making.