Abstract
Remote photoplethysmography (rPPG) is a promising non-invasive technique for measuring vital signs remotely, such as through videocalls. However, network and computing constraints can significantly compromise its accuracy. In this study, we evaluated the effects of these constraints on rPPG methods using four reference datasets and a standard unsupervised rPPG signal extraction pipeline. Our experiments simulated the impact of frame dropping, streaming video at different resolutions and frame rates, and other resource limitations. We found that these constraints can significantly degrade rPPG accuracy, but implementing specific strategies (such as reconstructing the signal in the receiver) can mitigate these effects. For example, with a 20% of frame loss, our proposed strategies reduced the MAE increase from 539% to 29%. These findings highlight the importance of considering network and computing constraints in rPPG applications.
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This research has been supported by the Academy of Finland 6G Flagship program under Grant 346208 and PROFI5 HiDyn under Grant 326291.
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Álvarez Casado, C., Nguyen, L., Silvén, O., Bordallo López, M. (2023). Assessing the Feasibility of Remote Photoplethysmography Through Videocalls: A Study of Network and Computing Constraints. In: Gade, R., Felsberg, M., Kämäräinen, JK. (eds) Image Analysis. SCIA 2023. Lecture Notes in Computer Science, vol 13886. Springer, Cham. https://doi.org/10.1007/978-3-031-31438-4_38
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