Background Achieving high-precision, low-latency, and continuously adaptive human activity recognition on resource-constrained edge devices represents a core challenge. Existing research primarily focuses on improvements in single directions, such as “online learning,” “model sparsification,” or “feature extraction,” lacking a framework that synergistically optimizes all three. This leads to difficulties in dynamically balancing accuracy, latency, and power consumption when processing non-stationary sensor data streams. Methods To address this, this paper designs an end-to-end closed-loop adaptive learning framework. The core innovation of this framework lies in its system-level synergistic design: (1) Employing fast principal component analysis for adaptive feature dimensionality reduction; (2) Introducing an information theory-based dynamic sparse subnetwork activation mechanism to tackle the NP-hard problem of model selection; and (3) Integrating a low-complexity online incremental learning module for real-time tracking of concept drift. Through the closed-loop feedback and control of the aforementioned components, this framework achieves joint dynamic optimization of feature extraction, model complexity, and adaptation speed under edge computing constraints. Results Experimental results across five datasets demonstrate that this framework achieves accuracies ranging from 85.6% to 97.4%, with inference latency of approximately 1.0 ms. Conclusion The framework comfortably meets the real-time requirement.
Li et al. (Thu,) studied this question.