CardioLive: Empowering Video Streaming with Online Cardiac Monitoring
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915664303751168 |
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| author | Lyu, Sheng Huang, Ruiming Ji, Sijie Rehman, Yasar Abbas Ur Ma, Lan Wu, Chenshu |
| author_facet | Lyu, Sheng Huang, Ruiming Ji, Sijie Rehman, Yasar Abbas Ur Ma, Lan Wu, Chenshu |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_00702 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CardioLive: Empowering Video Streaming with Online Cardiac Monitoring Lyu, Sheng Huang, Ruiming Ji, Sijie Rehman, Yasar Abbas Ur Ma, Lan Wu, Chenshu Human-Computer Interaction Networking and Internet Architecture Sound Audio and Speech Processing Image and Video Processing 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. |
| title | CardioLive: Empowering Video Streaming with Online Cardiac Monitoring |
| topic | Human-Computer Interaction Networking and Internet Architecture Sound Audio and Speech Processing Image and Video Processing |
| url | https://arxiv.org/abs/2502.00702 |