Agile human activity recognition for wearable devices based on online incremental learning

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.