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.

翻译：认知-运动发育异常的早期检测对及时干预至关重要，但传统评估方法严重依赖主观、静态的评价。数字设备的整合为通过数字生物标志物实现连续、客观监测提供了契机。本研究提出了一种基于人工智能的纵向框架，用于建模18个月至8岁儿童的发育轨迹。利用跨多个学年收集的平板交互数据集，我们分析了六项认知-运动任务（如精细运动控制、反应时间）。通过降维（t-SNE）和无监督聚类（K-Means++）识别出不同的发育表型，并追踪个体随时间在各类表型间的状态转移。分析揭示了三种显著表型：低表现、中表现和高表现。关键的是，纵向追踪显示低表现集群具有高度稳定性（早期保留率>90%），提示早期缺陷若无干预往往持续存在。相反，高表现集群表现出更大变异性，可能反映参与度因素。本研究验证了利用触屏数据通过无监督学习揭示异质性发育路径的可行性。所识别的表型可作为认知发展的可扩展、数据驱动的代理指标，为早期筛查工具和个性化儿科干预奠定基础。