Applications in security, healthcare, and human-computer interaction critically require accurate gait identification under complex environmental conditions such as varying lighting and background noise. Current approaches are usually unable to adapt to dynamic, highdimensional environments, with reduced accuracy of feature extraction and classification. This paper bridges the gap by offering an overview of a multi-stage framework that merges the advanced techniques of machine learning with those of reinforcement learning for preemptive optimization. It begins by using Deep Deterministic Policy Gradient for a preprocessing module: environmental parameters are dynamically adjusted so that their real-time data quality is optimized. The module is then followed by a phase in multi-domain feature extraction using Sparse Group Lasso along with KMeans clustering, thereby improving representativeness while reducing dimensionality by 50–60%. We have used a hybrid of stacked generalization, in this case of XGBoost and LightGBM, because this provides a better overall classification accuracy. Refined temporal post-processing at the hidden Markov model and Auto-Regressive Integrated Moving Average (ARIMA) results in enhanced phase transitions that may be gait-based, thus improving the identification accuracy. As the final step, we use Proximal Policy Optimization to implement feedback-driven reinforcement learning, where improvements are incrementally made by updating the model with iterative feedback. This new method enhances the correctness of feature extraction by 12% in complex environments. Overall classification accuracy increases by 5–6% and reaches 95%. False positives in gait phase transitions decrease as well, further increasing the system robustness and reliability in real-world applications.
