[2603.25673] Longitudinal Digital Phenotyping for Early Cognitive-Motor Screening

Computer Science > Machine Learning arXiv:2603.25673 (cs) [Submitted on 26 Mar 2026] Title:Longitudinal Digital Phenotyping for Early Cognitive-Motor Screening Authors:Diego Jimenez-Oviedo, Ruben Vera-Rodriguez, Ruben Tolosana, Juan Carlos Ruiz-Garcia, Jaime Herreros-Rodriguez View a PDF of the paper titled Longitudinal Digital Phenotyping for Early Cognitive-Motor Screening, by Diego Jimenez-Oviedo and 3 other authors View PDF HTML (experimental) Abstract:Early detection of atypical cognitive-motor development is critical for timely intervention, yet traditional assessments rely heavily on subjective, static evaluations. The integration of digital devices offers an opportunity for continuous, objective monitoring through digital biomarkers. In this work, we propose an AI-driven longitudinal framework to model developmental trajectories in children aged 18 months to 8 years. Using a dataset of tablet-based interactions collected over multiple academic years, we analyzed six cognitive-motor tasks (e.g., fine motor control, reaction time). We applied dimensionality reduction (t-SNE) and unsupervised clustering (K-Means++) to identify distinct developmental phenotypes and tracked individual transitions between these profiles over time. Our analysis reveals three distinct profiles: low, medium, and high performance. Crucially, longitudinal tracking highlights a high stability in the low-performance cluster (>90% retention in early years), suggesting that early deficits tend to...