The field of gait analysis is witnessing significant advancements, particularly in the development of non-invasive, cost-effective, and sensitive tools for quantitative evaluation. Innovations are focusing on enhancing the precision and applicability of gait monitoring systems, with a notable shift towards integrating computer vision and wearable technologies. These systems are not only improving the accuracy of gait parameter measurements but also expanding the scope of analysis to include real-time, long-sequence data suitable for advanced machine learning models. Additionally, there is a growing emphasis on creating normative gait cycle parameters using human pose estimation, which promises to revolutionize clinical analysis by providing objective, multi-feature assessments of complex movements. This approach not only supports clinical decision-making but also automates the identification of gait abnormalities, thereby advancing rehabilitation and assistive technologies. Notably, the integration of synthetic data generation methods is addressing the challenges posed by small sample sizes in clinical studies, ensuring the stability and reliability of gait data analysis tools.