A contact-free inexpensive measurement system with an algorithm based on the integral form of video frames is proposed to estimate the respiration rate from an extracted respiration pattern. The proposed algorithm is applied and tested on 28 videos of sleeping-simulated positions, and the results are compared with the manual visual inspection values. With linear regression, the determination coefficient (${R^2}$) is 0.961, which demonstrates high agreement with reference measurements. In addition, the Bland–Altman plot shows that almost all data points are within the 95% limits of agreement. Moreover, the time complexity of the proposed algorithm, which involves taking just a single point value of the integral image, is lower than that of traditional methods that circulate over a large number of points. In other words, the proposed algorithm achieves $O({{1}})$ fixed-time complexity compared to $O({N^2})$ for traditional methods. The average speed of processing is enhanced by about 17.4%.

中文翻译：

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用于远程健康监测的高效视频呼吸模式和呼吸率监测

提出了一种具有基于视频帧积分形式的算法的非接触式廉价测量系统，用于从提取的呼吸模式中估计呼吸率。该算法在28个模拟睡眠姿势的视频上进行了应用和测试，并将结果与​​人工目视检查值进行了比较。使用线性回归，决定系数 ( ${R^2}$) 为 0.961，表明与参考测量值高度一致。此外，Bland-Altman 图显示几乎所有数据点都在 95% 的一致性范围内。此外，所提出的算法的时间复杂度只涉及积分图像的单个点值，低于在大量点上循环的传统方法的时间复杂度。换句话说，与传统方法的$O({N^2})$相比，所提出的算法实现了$O({{1}})$固定时间复杂度。处理的平均速度提高了约 17.4%。