Artificial Intelligence in Gait and Movement Analysis

Background: Movement and gait analysis is an important field in medicine, athletics, and even security. Traditional methods like motion capture, force plates, and wearable sensors, while great, are very expensive and require a specific environment in which to work. Because of this, they are not very accessible. These methods may take a long time to analyze. However, with the advent of newer methods of Artificial intelligence, we can automate feature detection, analyze in real time, and extract even more findings in more variables. Methods: For this study, a combination of different methodologies was used. For the qualitative analysis of the models, the author used data from the sensors, videos, and the clinical documentation of patients with neuromuscular and musculoskeletal disorders. The author used machine learning classifiers, convolutional neural networks, and long short-term memory networks to analyze and classify gait and predict rehabilitation potential in order to handle performance evaluation in terms of accuracy, sensitivity, specificity and robustness. Results: AI-based systems consistently outperformed traditional methods. Clinical applications demonstrated earlier detection of neurological disorders, wearable AI devices improved rehabilitation adherence, and computer vision models showed resilience under variable conditions such as lighting and clothing. In sports, AI provided real time biomechanical feedback to reduce injury risk and enhance performance. In security, AI-driven gait recognition strengthened biometric identification by adapting to environmental variability. Conclusion: AI is not merely an incremental improvement but a paradigm shift in gait and movement analysis. By bridging laboratory precision with real-world scalability, AI offers inclusive, accessible, and transformative solutions. Future directives emphasize the need for diverse datasets, explainable models, ethical safeguards, and collaborative research to ensure that AI-driven gait analysis remains human-centered and universally beneficial.