论文标题
演示：Rhythmedge：在边缘实现非接触式心率估计
Demo: RhythmEdge: Enabling Contactless Heart Rate Estimation on the Edge
论文作者
论文摘要
在此演示论文中，我们设计和原型Rhythmedge是一种低成本，基于深度学习的无接触系统，用于常规的HR监测应用。节奏通过促进无接触性质，实时/离线操作，廉价和可用的传感组件以及计算设备来对现有方法的好处。我们的Rhythmedge系统是可移植的，可以轻松部署，以在中等控制的室内或室外环境中可靠的人力资源估计。 Rhythmedge通过检测面部视频（远程照相学； RPPG）的血液体积的变化来测量人力资源，并使用现成的商业可用资源约束的边缘平台和摄像机进行即时评估。我们通过将Rhythmedge的可扩展性，灵活性和兼容性通过在不同的不同体系结构的三个资源约束平台上（Nvidia Jetson Nano，Google Coral Development Board，Raspberry Pi）和三个异构摄像机进行分配到不同的资源约束平台上，并具有不同的敏感性，分辨率，propertal，propertal，property，Action Camper和Dslr）。 Rhythmedge进一步存储纵向心血管信息，并为用户提供即时通知。我们通过分析其运行时，内存和功率使用情况来彻底测试三个边缘计算平台的原型稳定性，延迟和可行性。
In this demo paper, we design and prototype RhythmEdge, a low-cost, deep-learning-based contact-less system for regular HR monitoring applications. RhythmEdge benefits over existing approaches by facilitating contact-less nature, real-time/offline operation, inexpensive and available sensing components, and computing devices. Our RhythmEdge system is portable and easily deployable for reliable HR estimation in moderately controlled indoor or outdoor environments. RhythmEdge measures HR via detecting changes in blood volume from facial videos (Remote Photoplethysmography; rPPG) and provides instant assessment using off-the-shelf commercially available resource-constrained edge platforms and video cameras. We demonstrate the scalability, flexibility, and compatibility of the RhythmEdge by deploying it on three resource-constrained platforms of differing architectures (NVIDIA Jetson Nano, Google Coral Development Board, Raspberry Pi) and three heterogeneous cameras of differing sensitivity, resolution, properties (web camera, action camera, and DSLR). RhythmEdge further stores longitudinal cardiovascular information and provides instant notification to the users. We thoroughly test the prototype stability, latency, and feasibility for three edge computing platforms by profiling their runtime, memory, and power usage.
