SkinMap: Weighted Full-Body Skin Segmentation for Robust Remote Photoplethysmography
This work addresses the need for robust, contactless vital sign monitoring in applications like remote patient care and smart vehicles, though it appears incremental by building on existing unsupervised pipelines.
The paper tackles the problem of improving remote photoplethysmography (rPPG) for heart rate monitoring by introducing a novel skin segmentation technique that prioritizes skin regions to enhance signal quality, resulting in maintained mean absolute error (MAE) in challenging conditions like talking and head rotation where other methods fail.
Remote photoplethysmography (rPPG) is an innovative method for monitoring heart rate and vital signs by using a simple camera to record a person, as long as any part of their skin is visible. This low-cost, contactless approach helps in remote patient monitoring, emotion analysis, smart vehicle utilization, and more. Over the years, various techniques have been proposed to improve the accuracy of this technology, especially given its sensitivity to lighting and movement. In the unsupervised pipeline, it is necessary to first select skin regions from the video to extract the rPPG signal from the skin color changes. We introduce a novel skin segmentation technique that prioritizes skin regions to enhance the quality of the extracted signal. It can detect areas of skin all over the body, making it more resistant to movement, while removing areas such as the mouth, eyes, and hair that may cause interference. Our model is evaluated on publicly available datasets, and we also present a new dataset, called SYNC-rPPG, to better represent real-world conditions. The results indicate that our model demonstrates a prior ability to capture heartbeats in challenging conditions, such as talking and head rotation, and maintain the mean absolute error (MAE) between predicted and actual heart rates, while other methods fail to do so. In addition, we demonstrate high accuracy in detecting a diverse range of skin tones, making this technique a promising option for real-world applications.