The increasing incidence of neurological disorders among older adults, including neuromotor and neurosomatic conditions, underscores the critical need for early detection and continuous monitoring. These disorders significantly impact the aging population's quality of life, making early diagnosis and man-agement essential for maintaining independence and functionality in daily activities. Recent advancements in smart sensor technologies offer promising avenues for non-intrusive monitoring within living environments, enhancing the ability to detect subtle changes in health status over time. This paper explores the innovative application of a single RGB-D camera for gait analysis in the elderly, aiming to facilitate early detection and follow-up of neurological disorders, such as Parkin-son's and Huntington's Diseases, fibromyalgia, and migraine. Gait, as an integral component of the activities of daily living, provides valuable insights into neurological health. Our study leverages Long Short-Term Memory models to classify gait patterns among individuals with neurological disorders compared to healthy controls, introducing a novel Gait Quality Index (GQI) for assessing gait integrity and progression of disorders. Conducted on a diverse cohort of 194 participants, including those diagnosed with the specified neurological disorders and healthy individuals, our experiment showcases the potential of RGB-D camera-based gait analysis. The classification model achieved a remarkable accuracy rate exceeding 93%, while the GQI demonstrated high precision in evaluating gait quality, with accuracy rates of 94% for Parkinson's and 88% for migraine …