Abstract
In computer vision, human pose estimation (HPE) through convolutional neural networks (CNNs) has emerged as a promising avenue with broad applicability. This study bridges a novel application of HPE, targeting the early detection of Alzheimer’s disease (AD), a condition expected to affect roughly 13.4 million Americans by 2026. Traditionally, AD diagnostic methodologies like brain imaging, Electroencephalography, and blood/neuropsychological tests are not only expensive and protracted but also require specialized medical expertise. Addressing these constraints, we introduce a cost-efficient and universally accessible system to detect AD, harnessing conventional cameras and employing pose estimation, signal processing, and machine learning. Data was sourced from videos capturing a 10-meter curve walk of 73 cognitively healthy older adults (HC) and 34 AD patients. The recording apparatus was a camera offering a resolution of 1920x1080 pixels at 30 frames/second, stationed laterally to the walking path. Using OpenPose, a state-of-the-art, bottom-up multi-person HPE method based on CNNs, we derived 25 distinctive body joint coordinates from the footage. Subsequently, 48 gait parameters were extracted from these joints and subjected to statistical scrutiny. A noticeable difference was observed in 39 out of the 48 gait parameters between the HC and AD groups. Leveraging a Support Vector Machine (SVM) to classify the data, the distinctiveness of these gait markers was further affirmed. The system accomplished a commendable accuracy rate of 90.01% and an F-score of 86.20% for AD identification. In essence, our findings advocate that the amalgamation of everyday cameras, sophisticated HPE techniques, signal processing, and machine learning can pave the way for practical AD detection in non-specialized settings, including home environments.