A Review on the Developments in the Field of AI-Based Gait Analysis

The examination of human gait is essential for comprehending motor function, the evolution of diseases, and the effectiveness of rehabilitation in different clinical and research environments. Artificial Intelligence (AI) advancements have recently improved gait analysis methodologies, resulting in increased accuracy, efficiency, and therapeutic effectiveness. This review article compiles the existing literature on AI-based gait analysis. The review emphasizes the role of AI in gait analysis, namely via the use of vision-based approaches, wearable sensor technologies, markerless motion capture systems, and inertial sensor-based methodologies. AI has shown potential in identifying musculoskeletal problems including osteopenia and sarcopenia, forecasting the start of Parkinson's disease, assessing the effects of post-stroke rehabilitation, and diagnosing neurological conditions. In addition, the integration of AI with new technologies such as augmented reality (AR), smart sensors, and wearable devices presents intriguing opportunities to improve gait rehabilitation, enable individualized therapies, and facilitate remote patient monitoring. Although there have been significant breakthroughs, there are still obstacles in creating AI models that can be easily understood for clinical decision-making, establishing standardized procedures for collecting data, and verifying AI algorithms across various populations. This study underlines the transformational potential of AI-based gait analysis in customized healthcare, rehabilitation techniques, and clinical results. In order to fully harness the potential of AI in enhancing human health and well-being, it is imperative to do more research and foster multidisciplinary cooperation.