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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高效的基于视频的呼吸模式和呼吸频率监控，用于远程健康监控

提出了一种非接触式廉价测量系统，该系统采用基于视频帧整体形式的算法，可从提取的呼吸模式中估算呼吸速率。将该算法应用于28个模拟睡眠位置的视频，并进行了测试，并将结果与​​人工目测值进行了比较。通过线性回归，确定系数（$ {R ^ 2} $）为0.961，与参考测量值高度吻合。此外，布兰德-奥尔特曼图表明，几乎所有数据点均在协议的95％范围内。此外，所提出的算法的时间复杂度仅涉及积分图像的单个点值，它比在大量点上循环的传统方法的时间复杂度低。换句话说，与传统方法的$ O（{N ^ 2}）$相比，该算法实现了$ O（{{{1}}）$的固定时间复杂度。平均处理速度提高了约17.4％。