Integrated SimpleHRNet-Based Pose Detection and LSTM-Enhanced Abnormal Gait Analysis: A Comprehensive Framework for Human Movement Understanding

The emergence of modern artificial intelligence has been integral to many applications in healthcare and gait analysis has been no exception to this. In this paper, we propose a comprehensive approach to gait analysis that leverages sophisticated computer vision techniques and deep learning models. Foremost we have used high-resolution human pose detection model, SimpleHRNet to obtain fine-grained body k eypoints in real-time and also for uploaded videos frame by frame. We then extract gait parameters, stride length in particular, from the spatial dependencies of relevant joints in order to obtain critical insight into human locomotion dynamics. Next, we have enhanced LSTM model to detect abnormal gait by learning temporal patterns from the assembled dataset. We evaluate our approach using quantitative metrics such as Root Mean Squared Error (RMSE), and qualitative visualizations suggesting that the proposed approach can accurately predict gait abnormalities. Post-processing steps refine t he data for improved interpretability, contributing t o a holistic understanding of the model's performance. The study emphasizes ethical considerations, responsible AI practices, and potential applications in healthcare and biomechanics. The results offer a nuanced analysis of gait patterns, fostering a deeper comprehension of human movement through the synergy of state-of-the-art technology and biomechanical insights.