Adaptive Feedback-Driven Segmentation for Continuous Multi-Label Human Activity Recognition

Radar-based continuous human activity recognition (HAR) in realistic scenarios faces challenges in segmenting and classifying overlapping or concurrent activities. This paper introduces a feedback-driven adaptive segmentation framework for multi-label classification in continuous HAR, leveraging Bayesian optimization (BO) and reinforcement learning (RL) to dynamically adjust segmentation parameters such as segment length and overlap in the data stream, optimizing them based on performance metrics such as accuracy and F1-score. Using a public dataset of continuous human activities, the method trains ResNet18 models on spectrogram, range-Doppler, and range-time representations from a 20% computational subset. Then, it scales optimized parameters to the full dataset. Comparative analysis against fixed-segmentation baselines was made. The results demonstrate significant improvements in classification performance, confirming the potential of adaptive segmentation techniques in enhancing the accuracy and efficiency of continuous multi-label HAR systems.