Remote Blood Pressure Estimation via the Spatiotemporal Mapping of Facial Videos

Abstract

Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world.

Fair non-contact blood pressure estimation using imaging photoplethysmography

“…The comparative data were obtained from the accuracies of the best models and methods identified in existing literature, as illustrated in Table 3 . Despite the complexity of the dataset employed in this study, which originates from diverse natural environments, and covers various age groups, different ethnicities, and a wide range of skin tones, the method employed yielded an MAE of 12.40 for measuring SBP, second only to the 12.35 achieved by Chen et al [ 7 ]. As for measuring DBP, the MAE was 5.74, demonstrating a notable improvement compared to existing methods.…”

“…In a similar vein, Yuheng Chen et al [ 7 ] proposed a methodology involving the conversion of facial videos from RGB format to an enhanced YUV format, which separates color from luminance and enhances blood pressure prediction accuracy. They developed a ResNet18+BiLSTM model, achieving Mean Absolute Errors (MAE) of 12.35 and 9.54 for SBP and DBP, respectively, on the MMSE-HR dataset [ 8 ].…”

Video-based Estimation of Blood Pressure

“…V-BPE computes different PTT values to independently estimate the systolic and diastolic blood pressure values. Studies by Hamoud et al [42] Chen et al [43], Cheng et al [44] and Xing et al [45] explore deep learning models such as Convolutional Neural Networks and Recurrent Neural Networks to estimate the blood pressure, utilizing information from facial videos and ECG signals. Mehta et al have reported that deep learning approaches of estimating vital signs face challenges such as over-constraining the task (the distribution of ground truth is limited to a certain range) and data leakage (where data similar to the training data is present in the test data) [36].…”

“…• The creation of the Face-Hand dataset consisting of videos and demographic data of subjects with their face and hand in the same video frame, to facilitate PTT-based blood pressure estimation in outside-the-lab settings Hassan et al [32] 2008 ✓ S Bang et al [21] 2009 ✓ ✓ S Gesche et al [13] 2012 ✓ ✓ S Chen et al [20] 2012 ✓ S, D Ma et al [12] 2014 ✓ ✓ S, D Wang et al [30] 2014 ✓ S, D Buxi et al [14] 2015 ✓ S Jeong & Finkelstein [34] 2016 ✓ S Liu et al [31] 2018 ✓ S Chandrasekhar et al [23] 2018 ✓ S, D Luo et al [35] 2019 ✓ ✓ S, D Finnegan et al [33] 2023 ✓ S, D Hamoud et al [42] 2023 ✓ S, D Chen et al [43] 2023 ✓ S, D Cheng et al [44] 2023 ✓ S, D Xing et al [45] 2023 3 Methods…”

Heart Rate Estimation from Facial Image Sequences of a Dual-Modality RGB-NIR Camera

“…Vital signs monitoring (such as the temperature, heart rate (HR), respiration, blood pressure (BP) [1], pulse rate variability (PRV) [2], etc.) is important for daily care of the elderly or patients.…”