School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK.
School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK.
Sensors (Basel). 2023 May 7;23(9):4550. doi: 10.3390/s23094550.
Detecting vital signs by using a contactless camera-based approach can provide several advantages over traditional clinical methods, such as lower financial costs, reduced visit times, increased comfort, and enhanced safety for healthcare professionals. Specifically, Eulerian Video Magnification (EVM) or Remote Photoplethysmography (rPPG) methods can be utilised to remotely estimate heart rate and respiratory rate biomarkers. In this paper two contactless camera-based health monitoring architectures are developed using EVM and rPPG, respectively; to this end, two different CNNs, (Mediapipe's BlazeFace and FaceMesh) are used to extract suitable regions of interest from incoming video frames. These two methods are implemented and deployed on four off-the-shelf edge devices as well as on a PC and evaluated in terms of latency (in each stage of the application's pipeline), throughput (FPS), power consumption (Watt), efficiency (throughput/Watt), and value (throughput/cost). This work provides important insights about the computational costs and bottlenecks of each method on each hardware platform, as well as which platform to use depending on the target metric. One of our insights shows that the Jetson Xavier NX platform is the best platform in terms of throughput and efficiency, while Raspberry Pi 4 8 GB is the best platform in terms of value.
利用非接触式摄像头的方法来检测生命体征，可以为医疗保健专业人员提供比传统临床方法更多的优势，例如更低的财务成本、减少就诊次数、增加舒适度和提高安全性。具体来说，可以利用欧拉视频放大（Eulerian Video Magnification，EVM）或远程光电容积描记法（Remote Photoplethysmography，rPPG）方法来远程估计心率和呼吸率生物标志物。在本文中，使用 EVM 和 rPPG 分别开发了两种基于非接触式摄像头的健康监测架构；为此，使用了两种不同的卷积神经网络（Mediapipe 的 BlazeFace 和 FaceMesh），从输入的视频帧中提取合适的感兴趣区域。这两种方法在四个现成的边缘设备以及个人电脑上进行了实现和部署，并根据延迟（应用程序管道的每个阶段）、吞吐量（FPS）、功耗（Watt）、效率（吞吐量/Watt）和价值（吞吐量/成本）进行了评估。这项工作提供了关于每个方法在每个硬件平台上的计算成本和瓶颈的重要见解，以及根据目标指标应该使用哪个平台。我们的一个见解表明，Jetson Xavier NX 平台在吞吐量和效率方面是最佳平台，而 Raspberry Pi 4 8GB 在价值方面是最佳平台。
