Video-Based Gait Analysis System for Early Detection of Cognitive Impairment, Alzheimer's Disease, and Related Disorders

Abstract

Cognitive impairments such as Alzheimer's disease (AD) and mild cognitive impairment (MCI) are major public health challenges, yet early detection remains difficult. Current solutions, including neuropsychological assessments, brain imaging (MRI, PET), and blood-based biomarker tests, are expensive, invasive, and require specialized facilities and personnel. These methods often result in late diagnoses, limited accessibility in primary care or community settings, and high patient burden. As a result, millions of older adults remain undiagnosed until disease progression has already compromised treatment effectiveness.

Researchers at Florida Atlantic University have developed a novel, non-invasive system that uses standard cameras (such as smartphones or tablets) combined with advanced computer vision and machine learning algorithms to detect early signs of Alzheimer's disease and related cognitive impairments through gait and balance analysis. Unlike current diagnostic methods, this technology requires no specialized facilities, expensive imaging equipment, or body-worn sensors. The system has demonstrated up to 90% accuracy in distinguishing patients with cognitive impairment from healthy individuals in clinical studies. The technology is currently at the proof-of-concept stage, supported by experimental validation with patient populations, and is positioned for further development toward clinical and commercial applications.

FAU seeks to advance this innovation into the marketplace through licensing or development partnerships.

Benefit

Market Application

Publications

Mild Cognitive Impairment Detection Through Gait Analysis and Standard Cameras

Human Pose Estimation and Gait Analysis with Convolutional Neural Networks for Alzheimer's Disease Detection