Department of Electronic Engineering, Group of Biomedical and Electronic Instrumentation, Universitat Politècnica de Catalunya, Jordi Girona, 1-3, 08034, Barcelona, Spain.
Biomed Eng Online. 2018 Jan 29;17(1):11. doi: 10.1186/s12938-018-0437-0.
In the last few years, some studies have measured heart rate (HR) or heart rate variability (HRV) parameters using a video camera. This technique focuses on the measurement of the small changes in skin colour caused by blood perfusion. To date, most of these works have obtained HRV parameters in stationary conditions, and there are practically no studies that obtain these parameters in motion scenarios and by conducting an in-depth statistical analysis.
In this study, a video pulse rate variability (PRV) analysis is conducted by measuring the pulse-to-pulse (PP) intervals in stationary and motion conditions. Firstly, given the importance of the sampling rate in a PRV analysis and the low frame rate of commercial cameras, we carried out an analysis of two models to evaluate their performance in the measurements. We propose a selective tracking method using the Viola-Jones and KLT algorithms, with the aim of carrying out a robust video PRV analysis in stationary and motion conditions. Data and results of the proposed method are contrasted with those reported in the state of the art.
The webcam achieved better results in the performance analysis of video cameras. In stationary conditions, high correlation values were obtained in PRV parameters with results above 0.9. The PP time series achieved an RMSE (mean ± standard deviation) of 19.45 ± 5.52 ms (1.70 ± 0.75 bpm). In the motion analysis, most of the PRV parameters also achieved good correlation results, but with lower values as regards stationary conditions. The PP time series presented an RMSE of 21.56 ± 6.41 ms (1.79 ± 0.63 bpm).
The statistical analysis showed good agreement between the reference system and the proposed method. In stationary conditions, the results of PRV parameters were improved by our method in comparison with data reported in related works. An overall comparative analysis of PRV parameters in motion conditions was more limited due to the lack of studies or studies containing insufficient data analysis. Based on the results, the proposed method could provide a low-cost, contactless and reliable alternative for measuring HR or PRV parameters in non-clinical environments.
在过去的几年中，一些研究已经使用摄像机测量心率（HR）或心率变异性（HRV）参数。这项技术专注于测量由血液灌注引起的皮肤颜色的微小变化。迄今为止，这些工作中的大多数都是在静止状态下获得 HRV 参数的，实际上没有研究是在运动场景中获得这些参数并进行深入的统计分析。
在这项研究中，通过测量静止和运动条件下的脉搏到脉搏（PP）间隔来进行视频脉搏率变异性（PRV）分析。首先，鉴于 PRV 分析中采样率的重要性和商用摄像机的低帧率，我们对两种模型进行了分析，以评估它们在测量中的性能。我们提出了一种使用 Viola-Jones 和 KLT 算法的选择性跟踪方法，旨在对静止和运动条件下进行稳健的视频 PRV 分析。提出的方法的数据和结果与现有技术的报告结果进行了对比。
网络摄像头在视频摄像机的性能分析中取得了更好的结果。在静止条件下，PRV 参数的相关性值较高，结果高于 0.9。PP 时间序列的 RMSE（平均值±标准差）为 19.45±5.52ms（1.70±0.75bpm）。在运动分析中，大多数 PRV 参数也取得了较好的相关性结果，但与静止条件相比，值较低。PP 时间序列的 RMSE 为 21.56±6.41ms（1.79±0.63bpm）。
统计分析表明，参考系统与提出的方法之间具有良好的一致性。在静止条件下，与相关工作中报告的数据相比，我们的方法提高了 PRV 参数的结果。由于缺乏研究或研究中包含的数据分析不足，对运动条件下 PRV 参数的整体比较分析受到了更多限制。基于这些结果，提出的方法可以为非临床环境中测量 HR 或 PRV 参数提供一种低成本、非接触式和可靠的替代方法。
