Bhutani Saksham, Elgendi Mohamed, Menon Carlo
Biomedical and Mobile Health Technology Research Lab, ETH Zürich, Zürich, Switzerland.
Department of Biomedical Engineering and Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE.
Commun Eng. 2025 Apr 7;4(1):66. doi: 10.1038/s44172-025-00363-z.
The revolutionary remote photoplethysmography (rPPG) technique has enabled intelligent devices to estimate physiological parameters with remarkable accuracy. However, the continuous and surreptitious recording of individuals by these devices and the collecting of sensitive health data without users' knowledge or consent raise serious privacy concerns. Here we explore frugal methods for modifying facial videos to conceal physiological signals while maintaining image quality. Eleven lightweight modification methods, including blurring operations, additive noises, and time-averaging techniques, were evaluated using five different rPPG techniques across four activities: rest, talking, head rotation, and gym. These rPPG methods require minimal computational resources, enabling real-time implementation on low-compute devices. Our results indicate that the time-averaging sliding frame method achieved the greatest balance between preserving the information within the frame and inducing a heart rate error, with an average error of 22 beats per minute (bpm). Further, the facial region of interest was found to be the most effective and to offer the best trade-off between bpm errors and information loss.
革命性的远程光电容积脉搏波描记术（rPPG）技术使智能设备能够以极高的准确性估计生理参数。然而，这些设备对个人进行持续且秘密的记录，以及在用户不知情或未同意的情况下收集敏感健康数据，引发了严重的隐私问题。在此，我们探索一些节俭的方法来修改面部视频，以在保持图像质量的同时隐藏生理信号。我们使用五种不同的rPPG技术，针对休息、交谈、头部转动和健身这四项活动，评估了包括模糊操作、加性噪声和时间平均技术在内的十一种轻量级修改方法。这些rPPG方法所需的计算资源极少，能够在低算力设备上实时实现。我们的结果表明，时间平均滑动帧方法在保留帧内信息和引发心率误差之间实现了最大平衡，平均误差为每分钟22次心跳（bpm）。此外，发现面部感兴趣区域最为有效，并且在bpm误差和信息损失之间提供了最佳权衡。
