rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement
This work addresses the challenge of acquiring labeled data for remote physiological measurement, offering a noise-insensitive solution that could benefit healthcare monitoring applications, though it appears incremental as it builds on existing self-supervised techniques.
The paper tackles the problem of remote photoplethysmography (rPPG) for measuring vital signs by proposing a self-supervised pre-training method with masked autoencoders to overcome limitations of supervised and contrastive learning approaches, achieving state-of-the-art performance on datasets like VIPL-HR, PURE, and UBFC-rPPG.
Remote photoplethysmography (rPPG) is an important technique for perceiving human vital signs, which has received extensive attention. For a long time, researchers have focused on supervised methods that rely on large amounts of labeled data. These methods are limited by the requirement for large amounts of data and the difficulty of acquiring ground truth physiological signals. To address these issues, several self-supervised methods based on contrastive learning have been proposed. However, they focus on the contrastive learning between samples, which neglect the inherent self-similar prior in physiological signals and seem to have a limited ability to cope with noisy. In this paper, a linear self-supervised reconstruction task was designed for extracting the inherent self-similar prior in physiological signals. Besides, a specific noise-insensitive strategy was explored for reducing the interference of motion and illumination. The proposed framework in this paper, namely rPPG-MAE, demonstrates excellent performance even on the challenging VIPL-HR dataset. We also evaluate the proposed method on two public datasets, namely PURE and UBFC-rPPG. The results show that our method not only outperforms existing self-supervised methods but also exceeds the state-of-the-art (SOTA) supervised methods. One important observation is that the quality of the dataset seems more important than the size in self-supervised pre-training of rPPG. The source code is released at https://github.com/linuxsino/rPPG-MAE.