Unsupervised Motion-Robust Self-Distillation Framework for Remote Physiological Measurement

Abstract

Remote photoplethysmography (rPPG) holds great potential in medical surveillance. However, head movements commonly encountered in real-world scenarios often degrade physiological estimation performance, particularly for unsupervised learning methods based on physiological frequency band priors, which usually struggle to detect dynamic signals occurring within this band, thereby limiting their performance ceiling. In this paper, we propose a novel strategy to endow unsupervised learning methods with motion robustness. Specifically, we introduce a simple motion simulation technique, Sliding Crop, to incorporate dynamic signals. Based on this, we develop an unsupervised motion-robust self-distillation framework (UMoRo) with the existing unsupervised learning method, where the model leverages its own high-quality mappings of simple samples as pseudo-labels to guide the learning process of suppressing simulated motion artifacts in challenging samples, thus enhancing motion robustness in real-world movements. Experimental results on three public datasets show that our method achieves superior or competitive performance compared to state-of-the-art supervised methods, demonstrating outstanding motion robustness.