标题： CardioLive：通过在线心脏监测赋能视频流媒体 显示英文标题

标题： CardioLive: Empowering Video Streaming with Online Cardiac Monitoring

摘要： 在线心脏监测（OCM）成为下一代视频流平台的一项引人注目的增强功能。 它支持多种应用，包括远程医疗、在线情感计算和深度伪造检测。 然而，视频流中封装的生理信息长期以来被忽视。 本文介绍了我们在视频流平台中设计和实现的第一个在线心脏监测系统CardioLive。 我们利用自然共存的视频和音频流，并设计了CardioNet，首个视听网络来学习心脏序列。 它结合了多个独特设计以提取时序和频谱特征，在实际视频流条件下确保了鲁棒性能。 为了实现按需服务的在线心脏监测，我们将CardioLive实现为即插即用的中间件服务，并开发了系统解决方案来解决实际问题，包括帧率变化和不同步流。 进行了大量实验以证明我们系统的有效性。 我们的系统达到了1.79 BPM的平均绝对误差（MAE），分别比仅视频和仅音频的解决方案高出69.2%和81.2%。 当在Zoom和YouTube上实施时，我们的CardioLive服务实现了平均吞吐量分别为115.97 FPS和98.16 FPS。 我们认为我们的工作为视频流系统开辟了新的应用领域。 我们很快会发布代码。 显示英文摘要

摘要： Online Cardiac Monitoring (OCM) emerges as a compelling enhancement for the next-generation video streaming platforms. It enables various applications including remote health, online affective computing, and deepfake detection. Yet the physiological information encapsulated in the video streams has been long neglected. In this paper, we present the design and implementation of CardioLive, the first online cardiac monitoring system in video streaming platforms. We leverage the naturally co-existed video and audio streams and devise CardioNet, the first audio-visual network to learn the cardiac series. It incorporates multiple unique designs to extract temporal and spectral features, ensuring robust performance under realistic video streaming conditions. To enable the Service-On-Demand online cardiac monitoring, we implement CardioLive as a plug-and-play middleware service and develop systematic solutions to practical issues including changing FPS and unsynchronized streams. Extensive experiments have been done to demonstrate the effectiveness of our system. We achieve a Mean Square Error (MAE) of 1.79 BPM error, outperforming the video-only and audio-only solutions by 69.2% and 81.2%, respectively. Our CardioLive service achieves average throughputs of 115.97 and 98.16 FPS when implemented in Zoom and YouTube. We believe our work opens up new applications for video stream systems. We will release the code soon.