Survey on Handwriting Analysis for Early Detection of Alzheimer

Alzheimer’s Disease (AD), a debilitating neurodegenerative condition, progressively impairs cognitive abilities and motor coordination. Timely diagnosis is crucial for intervention, yet conventional approaches like neuroimaging and cerebrospinal fluid analysis remain costly, invasive, and often ineffective in early stages.Recent advancements highlight digital handwriting analysis as a viable, non-invasive alternative for detecting subtle cognitive deterioration linked to AD. This review systematically evaluates contemporary research on handwriting-based diagnostics, focusing on key metrics—pen pressure variability, stroke irregularities, micro-tremors, and spatiotemporal inconsistencies—that correlate with early-stage AD. We compare conventional statistical models with cutting-edge AI-driven techniques, assessing their efficacy, dataset dependencies, and feature-selection strategies. Furthermore, we identify gaps in current methodologies and suggest innovations to improve clinical adoption, such as integrating real-time digital pen data with multimodal biomarkers. By synthesizing these insights, this work serves as a roadmap for developing scalable, cost-effective tools to detect AD through handwriting anomalies. Index Terms— Alzheimer’s disease, early detection, handwriting analysis, cognitive decline, digital health, pen-based monitoring, machine learning, motor function assessment, behavioral biomarkers, neurodegenerative disorders.