Longitudinal Digital Phenotyping for Early Cognitive-Motor Screening

TL;DR

This study demonstrates that AI-driven analysis of longitudinal touchscreen interaction data can identify distinct developmental profiles in children, providing a scalable method for early cognitive-motor screening and intervention planning.

Contribution

It introduces a novel longitudinal framework using unsupervised learning on digital biomarkers to classify developmental trajectories in children aged 18 months to 8 years.

Findings

Three distinct developmental profiles identified: low, medium, high performance.

High stability (>90%) in low-performance group over time.

Profiles serve as scalable proxies for cognitive development.

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 persist without intervention. Conversely, higher-performance clusters show greater variability, potentially reflecting engagement factors. This study validates the use of unsupervised learning on touchscreen data to uncover heterogeneous developmental paths. The identified profiles serve as scalable, data-driven proxies for cognitive growth, offering a foundation for early screening tools and personalized pediatric interventions.