Le2i UMR6306, CNRS, Arts et Métiers, Univ. Bourgogne Franche-Comté, 21000, Dijon, France.
Biomed Eng Online. 2018 Feb 9;17(1):22. doi: 10.1186/s12938-018-0450-3.
Remote photoplethysmography (rPPG) has been in the forefront recently for measuring cardiac pulse rates from live or recorded videos. It finds advantages in scenarios requiring remote monitoring, such as medicine and fitness, where contact based monitoring is limiting and cumbersome. The blood volume pulse, defined as the pulsative flow of arterial blood, gives rise to periodic changes in the skin color which are then quantified to estimate a temporal signal. This temporal signal can be analysed using various methods to extract the representative cardiac signal.
We present a novel method for measuring rPPG signals using constrained independent component analysis (cICA). We incorporate a priori information into the cICA algorithm to aid in the extraction of the most prominent rPPG signal. This a priori information is implemented using two constraints: first, based on periodicity using autocorrelation, and second, a chrominance-based constraint exploiting the physical characteristics of the skin.
Our method showed improved performances over traditional blind source separation methods like ICA and chrominance based methods with mean absolute errors of 0.62 beats per minute (BPM) and 3.14 BPM for the two datasets in our inhouse video database UBFC-RPPG, and 4.69 BPM for the public MMSE-HR dataset. Its performance was also better in comparison to other state of the art methods in terms of accuracy and robustness. Our UBFC-RPPG database is also made publicly available and is specifically aimed towards testing rPPG measurements.
远程光体积描记术（rPPG）最近在从实时或录制的视频中测量心搏率方面处于领先地位。它在需要远程监测的场景中具有优势，例如医学和健身领域，在这些领域中，基于接触的监测具有局限性和繁琐性。血液体积脉搏定义为动脉血液的脉动流动，它会引起皮肤颜色的周期性变化，然后对这些变化进行量化，以估算时间信号。可以使用各种方法分析该时间信号，以提取代表性的心脏信号。
我们提出了一种使用约束独立成分分析（cICA）测量 rPPG 信号的新方法。我们将先验信息纳入 cICA 算法中，以帮助提取最显著的 rPPG 信号。该先验信息通过两种约束来实现：首先，基于自相关的周期性；其次，利用皮肤物理特性的色度约束。
与传统的盲源分离方法（如 ICA 和基于色度的方法）相比，我们的方法在内部视频数据库 UBFC-RPPG 的两个数据集上的平均绝对误差分别为 0.62 次/分钟（BPM）和 3.14 BPM，在公共 MMSE-HR 数据集上的平均绝对误差为 4.69 BPM，性能表现更好。在准确性和鲁棒性方面，它也优于其他最先进的方法。我们的 UBFC-RPPG 数据库也公开发布，专门用于测试 rPPG 测量。
