Rochester Institute of Technology, Department of Electrical and Microelectronic Engineering, Communications Laboratory, 79 Lomb Memorial Drive, Building 9, Room 3050, Rochester, New York 14623-5603, USA.
J Biomed Opt. 2012 Jul;17(7):077011. doi: 10.1117/1.JBO.17.7.077011.
Nonobtrusive pulse rate measurement using a webcam is considered. We demonstrate how state-of-the-art algorithms based on independent component analysis suffer from a sorting problem which hinders their performance, and propose a novel algorithm based on constrained independent component analysis to improve performance. We present how the proposed algorithm extracts a photoplethysmography signal and resolves the sorting problem. In addition, we perform a comparative study between the proposed algorithm and state-of-the-art algorithms over 45 video streams using a finger probe oxymeter for reference measurements. The proposed algorithm provides improved accuracy: the root mean square error is decreased from 20.6 and 9.5 beats per minute (bpm) for existing algorithms to 3.5 bpm for the proposed algorithm. An error of 3.5 bpm is within the inaccuracy expected from the reference measurements. This implies that the proposed algorithm provided performance of equal accuracy to the finger probe oximeter.
使用网络摄像头进行非侵入式心率测量的方法得到了研究。我们展示了基于独立成分分析的最先进算法如何受到排序问题的困扰，从而影响其性能，并提出了一种基于约束独立成分分析的新算法来提高性能。我们介绍了所提出的算法如何提取光体积描记图信号并解决排序问题。此外，我们使用手指探头血氧计进行参考测量，在 45 个视频流上对所提出的算法和最先进的算法进行了比较研究。所提出的算法提供了更高的准确性：与现有的算法相比，均方根误差从 20.6 和 9.5 次/分钟（bpm）降低到 3.5 bpm，对于所提出的算法。3.5 bpm 的误差在参考测量所预期的不准确性范围内。这意味着所提出的算法提供了与手指探头血氧计相同的准确性性能。
