PhysTTT: Accurate and Lightweight Cross-Domain Heart Rate Measurement with Test-Time Training

Yancheng Yao, Yifei Liu, Shangwei Zhu, Yu Wang, Le Yuan, Shaohua Liu

Keywords: rPPG, Heart rate measurement, Test-Time Training, Cross-domain

TL;DR: Measure heart rate using the test-time training method in cross-domain scenarios.

Abstract: Remote photoplethysmography (rPPG), a contactless technology for measuring physiological signals, holds significant promise for smart healthcare and affective computing. However, a key challenge for existing deep learning methods is the paradox between maintaining high measurement accuracy and ensuring low computational cost, especially in cross-domain scenarios. To address this, we propose PhysTTT, a novel and lightweight framework for heart rate measurement that integrates multiple 1D-CNNs with residual structures and a Test-Time Training (TTT) layer. Multi-time frame differences fusion and 1D-CNNs extract spatio-temporal features from facial video sequences by modeling subtle brightness variations, the TTT layer compresses the context information into a learnable vector space, enhancing the temporal modeling capability. Crucially, the TTT mechanism enables the model to adapt to unseen data distributions during inference, significantly boosting cross-domain generalization. Extensive experiments demonstrate that PhysTTT achieves state-of-the-art accuracy in both in-domain and cross-domain evaluations, offering an optimal balance of high performance, strong generalization, and low computational cost. Our code is publicly available at https://anonymous.4open.science/r/PhysTTT-B605/.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 7347