Dasari Ananyananda, Jeni Laszlo A, Tucker Conrad S
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States of America.
The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States of America.
PLoS One. 2025 Jan 30;20(1):e0311654. doi: 10.1371/journal.pone.0311654. eCollection 2025.
In this work, we propose a non-contact video-based approach that estimates an individual's blood pressure. The estimation of blood pressure is critical for monitoring hypertension and cardiovascular diseases such as coronary artery disease or stroke. Estimation of blood pressure is typically achieved using contact-based devices which apply pressure on the arm through a cuff. Such contact-based devices are cost-prohibitive as well as limited in their scalability due to the requirement of specialized equipment. The ubiquity of mobile phones and video-based capturing devices motivates the development of a non-contact blood pressure estimation method-Video-based Blood Pressure Estimation (V-BPE). We leverage the time difference of the blood pulse arrival at two different locations in the body (Pulse Transit Time) and the inverse relation between the blood pressure and the velocity of blood pressure pulse propagation in the artery to analytically estimate the blood pressure. Through statistical hypothesis testing, we demonstrate that Pulse Transit Time-based approaches to estimate blood pressure require knowledge of subject specific blood vessel parameters, such as the length of the blood vessel. We utilize a combination of computer vision techniques and demographic information (such as the height and the weight of the subject) to capture and incorporate the aforementioned subject specific blood vessel parameters into our estimation of blood pressure. We demonstrate the robustness of V-BPE by evaluating the efficacy of blood pressure estimation in demographically diverse, outside-the-lab conditions. V-BPE is advantageous in three ways; 1) it is non-contact-based, reducing the possibility of infection due to contact 2) it is scalable, given the ubiquity of video recording devices and 3) it is robust to diverse demographic scenarios due to the incorporation of subject specific information.
在这项工作中，我们提出了一种基于视频的非接触式方法来估计个体的血压。血压估计对于监测高血压和心血管疾病（如冠状动脉疾病或中风）至关重要。血压估计通常使用基于接触的设备来实现，这些设备通过袖带对手臂施加压力。这种基于接触的设备成本高昂，并且由于需要专门设备，其扩展性也有限。手机和基于视频的捕获设备的普及推动了一种非接触式血压估计方法——基于视频的血压估计（V-BPE）的发展。我们利用血液脉冲到达身体两个不同位置的时间差（脉搏传输时间）以及血压与动脉中血压脉冲传播速度之间的反比关系来分析估计血压。通过统计假设检验，我们证明基于脉搏传输时间的血压估计方法需要了解个体特定的血管参数，如血管长度。我们利用计算机视觉技术和人口统计学信息（如受试者的身高和体重）的组合来获取上述个体特定的血管参数，并将其纳入我们的血压估计中。我们通过在人口统计学上多样化的实验室外条件下评估血压估计的有效性来证明V-BPE的稳健性。V-BPE在三个方面具有优势：1）它是基于非接触的，降低了因接触而感染的可能性；2）它具有可扩展性，鉴于视频记录设备的普及；3）由于纳入了个体特定信息，它对不同的人口统计学场景具有稳健性。
