Design of an Integrated Model for Gait Identification Using DDPG, Sparse Group Lasso, and Stacked Generalization

Security, healthcare, and human–computer interaction applications require accurate gait identification under complex environmental conditions such as varying lighting and background noise. Current approaches are usually unable to adapt to dynamic, high-dimensional environments, with reduced feature extraction and classification accuracy. 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 a Deep Deterministic Policy Gradient (DDPG) for a preprocessing module: environmental parameters are dynamically adjusted to optimize their real-time data quality. 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 better 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 (PPO) 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 also decrease, increasing system robustness and reliability in real-world applications.