Human health can be critically affected by cardiovascular diseases, such as hypertension, arrhythmias, and stroke. Heart rate and blood pressure are important biometric information for the monitoring of cardiovascular system and early diagnosis of cardiovascular diseases. Existing methods for estimating the heart rate are based on electrocardiography and photoplethyomography, which require contacting the sensor to the skin surface. Moreover, catheter and cuff-based methods for measuring blood pressure cause inconvenience and have limited applicability. Therefore, in this thesis, we propose a vision-based method for estimating the heart rate and blood pressure. This thesis proposes a 2-stage deep learning framework consisting of a dual remote photoplethysmography network (DRP-Net) and bounded blood pressure network (BBP-Net). In the first stage, DRP-Net infers remote photoplethysmography (rPPG) signals for the acral and facial regions, and these phase-shifted rPPG signals are utilized to estimate the heart rate. In the second stage, BBP-Net integrates temporal features and analyzes phase discrepancy between the acral and facial rPPG signals to estimate SBP and DBP values. To improve the accuracy of estimating the heart rate, we employed a data augmentation method based on a frame interpolation model. Moreover, we designed BBP-Net to infer blood pressure within a predefined range by incorporating a scaled sigmoid function. Our method resulted in estimating the heart rate with the mean absolute error (MAE) of 1.78 BPM, reducing the MAE by 34.31 % compared to the recent method, on the MMSE-HR dataset. The MAE for estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 10.19 mmHg and 7.09 mmHg. On the V4V dataset, the MAE for the heart rate, SBP, and DBP were 3.83 BPM, 13.64 mmHg, and 9.4 mmHg, respectively.

翻译：心血管疾病（如高血压、心律失常和中风）会严重危害人类健康。心率和血压是监测心血管系统及早期诊断心血管疾病的重要生物特征信息。现有心率估计方法多基于心电图和光电容积描记术，需将传感器接触皮肤表面。此外，基于导管和袖带的血压测量方法存在不便性且适用性有限。因此，本文提出一种基于视觉的心率和血压估计方法。本文构建了一个两阶段深度学习框架，包含双通道远程光电容积描记网络（DRP-Net）和带边界约束的血压网络（BBP-Net）。第一阶段，DRP-Net推断四肢末端和面部区域的远程光电容积描记（rPPG）信号，并利用这些相位偏移的rPPG信号估算心率。第二阶段，BBP-Net整合时序特征并分析四肢末端与面部rPPG信号的相位差异，以估算收缩压（SBP）和舒张压（DBP）值。为提升心率估计精度，我们采用基于帧插值模型的数据增强方法。同时，通过引入缩放Sigmoid函数设计BBP-Net，使其能在预设范围内推断血压值。在MMSE-HR数据集上，本方法的心率估计平均绝对误差（MAE）为1.78 BPM，较最新方法降低34.31%；收缩压和舒张压的MAE分别为10.19 mmHg和7.09 mmHg。在V4V数据集上，心率、SBP和DBP的MAE分别为3.83 BPM、13.64 mmHg和9.4 mmHg。