BYHE：一个简单的端到端视频心率测量网络框架
BYHE: A Simple Framework for Boosting End-to-end Video-based Heart Rate Measurement Network
摘要：
基于远程光电容积描记（rPPG）的心率测量在医疗保健中发挥着重要作用，它通过非接触、少约束的方式从面部视频中估计心率。端到端神经网络是基于rPPG的心率估计方法的一个主要分支，其特点是直接从原始人脸视频中恢复包含足够心率信息的rPPG信号。然而，在相关数据集上存在一些容易被忽视的问题，如标签波的不确定时延和不确定包络形状，阻碍了端到端方法的有效训练。虽然许多新颖的和强大的网络被提出，迄今为止还没有系统的研究深入到这些问题。本文从心脏活动中常见的内在节律周期性自相似性的角度出发，提出了一种综合的方法--增强心跳估计（BYHE），包括新的标签表示、相应的网络调整和损失函数。BYHE可以很容易地移植到现有的端到端网络，并提高其训练效率。通过应用我们的方法，我们可以节省大量的时间，而无需进行繁重的手工操作，例如标记波对齐，这是以前端到端方法所必需的，同时提高了数据集的利用率。根据我们的实验，BYHE可以利用经典的端到端网络，在大多数使用的数据集上达到与那些最先进的方法竞争的性能。这种改进表明，选择清晰和有效的标签表示也是一个有前途的方向，更好的远程生理信号测量。
英文摘要：
Heart rate measuring based on remote photoplethysmography (rPPG) plays an important role in health caring, which estimates heart rate from facial video in a non-contact, less-constrained way. End-to-end neural network is a main branch of rPPG-based heart rate estimation methods, whose trait is recovering rPPG signal containing sufficient heart rate message from original facial video directly. However, there exists some easily neglected problems on relevant datasets which thwarting the efficient training of end-to-end methods, such as uncertain temporal delay and indefinite envelope shape of label waves. Although many novel and powerful networks are proposed, hitherto there are no systematic research digging into these problems. In this paper, from perspective of common intrinsic rhythm periodical self-similarity results from cardiac activities, we propose a comprehensive methodology, Boost Your Heartbeat Estimation (BYHE), including new label representations, corresponding network adjustments and loss functions. BYHE can be easily grafted on current end-to-end network and boost its training efficiency. By applying our methodology, we can save tremendous time without conducting laborious handworks, such as label wave alignment which is necessary for previous end-to-end methods, and meanwhile enhance the utilization on datasets. According to our experiments, BYHE can leverage classical end-to-end network to reach competitive performance against those state-of-the-art methods on mostly used datasets. Such improvement indicates selecting perspicuous and efficient label representation is also a promising direction towards better remote physiological signal measurement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
