Technology-Enhanced Dual-Task Testing for Alzheimer’s Disease and Related Dementias: A Review of Trends, Tools, and Emerging Directions

Abstract

Alzheimer’s disease and related dementias (ADRD) are growing global health challenges, projected to affect over 82 million people by 2030. Early diagnosis is essential, offering the potential to extend life expectancy by over 50% and reduce healthcare costs by up to $150,000 per patient. Dual-task (DT) testing—evaluating motor performance under cognitive load—has emerged as a promising, non-invasive method for early ADRD detection. This review provides a comprehensive synthesis of DT-based ADRD assessments from January 2010 to October 2025, integrating insights from engineering and clinical neuroscience. We explore a broad range of DT paradigms (e.g., gait, balance, upper-limb function), sensing technologies (e.g., wearable sensors, electronic walkways, infrared/depth cameras, video, tablets, and brain imaging tools like fMRI and fNIRS), and analytic approaches, from traditional statistics to deep learning. Emerging tools, including eye-tracking and AI-based video pose estimation, are also discussed. We critically examine methodological trends, highlight key findings, and identify current limitations. Emphasizing the need for equitable, scalable, and clinically viable DT systems, this review highlights the role of modern sensor and AI technologies in enhancing early ADRD detection. It serves as a key resource for engineers, data scientists, and clinicians developing technology-driven tools for early detection and monitoring of neurodegenerative diseases.