Key result
The proposed deep learning models for remote photoplethysmography improved heart rate measurement RMSE by up to 42.6% at night and 28.6% under head motion conditions compared to state-of-the-art.
Why the study?
Driving presents a noise-heavy environment for remote photoplethysmography heart rate monitoring due to vehicle motion and varying illumination.
Does a novel deep learning model for remote photoplethysmography improve heart rate estimation accuracy in driving scenarios compared to state-of-the-art methods?
Does a novel deep learning model for remote photoplethysmography improve heart rate estimation accuracy in driving scenarios compared to state-of-the-art methods?
This is the authors' abstract. We don't add key points for this paper.
Driver monitoring is a popular topic of investigation. Contactless vital-sign measurement via remote photoplethysmography (rPPG) is an easy and effective alternative to cumbersome wearable devices during driving. However, driving is a noise-heavy situation for remote vital-sign sensing, owing to the disturbances caused by jolted cars or varying illumination from time and weather variations. In this study, we propose deep learning models for rPPG construction and heart rate estimation that work for both red–green–blue (RGB) and monochrome near-infrared (NIR) images. The proposed method overcomes the adaptation issues commonly seen in deep-learning-based heart rate estimation approaches and achieves a significant improvement in robustness for heart rate measurement. To evaluate the performance thoroughly, in addition to a public dataset, we construct four testing driving sets for various practical conditions, including driving at night, on rainy days, and on long-term journeys. It is also worth noting that we conducted the assessment with a cross-dataset protocol to further verify the efficiency of our method. The experimental results on the five datasets show that our proposed method can perform better than the state-of-the-art method for both the RGB and NIR images, especially in some challenging cases. The root-mean-square error (RMSE) for the situation with additional head motion conditions can be improved by 28.6% with RGB images and by 21.91% with NIR images on the open dataset. Moreover, RMSE can be improved by up to 42.6% at night, 14.41% on rainy days, and 18.78% on long-term journeys.
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A novel deep-learning-based remote photoplethysmography method significantly improves the robustness and accuracy of contactless heart rate measurement in challenging driving scenarios.
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Chiu et al. (2023) studied Driver monitoring for heart rate estimation. Deep learning models for rPPG construction and heart rate estimation vs. State-of-the-art method was evaluated on Root-mean-square error (RMSE) for heart rate measurement (Improved by up to 42.6%). The proposed deep learning models for remote photoplethysmography improved heart rate measurement RMSE by up to 42.6% at night and 28.6% under head motion conditions compared to state-of-the-art.
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