基于摄像头（可见光)的心率测量方法, 能够以非接触的方式检测受试者心率, 无论在临床应用还是在家庭健康监护中都有着极大的应用前景。 但是, CMOS摄像头的行扫描方式采集图像以及计算机系统的图像采集时钟抖动都会影响此类心率测量的精度, 从而引入相位误差和系统随机误差。 本文针对这两大主要误差进行研究分析, 提出了消除相位误差的基于傅里叶变换的幅频叠加算法、 消除图像采集系统时钟抖动误差的基于时间标定的三次样条插值重构方法。 幅频叠加算法只在幅频域对信号进行处理, 能够忽略信号的相位影响, 从而消除相位误差。 时间标定的三次样条重构算法能够重构图像的均匀采集, 从而消除系统时钟抖动引起的随机误差。 同时, 通过理论推导论证了两种算法的可行性, 进而利用LED模拟实验和实际心率测量实验验证该算法在提高信号检测精密度中的特性。 在模拟实验中, 对每帧图像的200行作幅频叠加运算, 能够使信号幅值相对提升458%; 基于时间标定的三次样条重构算法能使检测的信号频率的均方根误差缩小30%以上; 在实际心率检测中, 幅频叠加算法可使心率幅值相对提升达335%, 基于时间标定的三次样条重构算法可使心率准确度提升40%左右。 因此, 模拟实验和实际心率测量实验均验证了提出的算法在提高系统检测精度上的有效性, 提高了基于机器视觉心率测量的抗干扰能力。 这两种算法不仅能够提高基于机器视觉的心率检测精度, 而且能够适用于基于机器视觉对一定频率信号的检测, 在机器视觉检测中具有广泛的意义。

The heart rate (HR) measurements based on the camera (visible light) can be used to detect HR in non-contact mode, which has great application prospects both in the clinical application and home health care. However, CMOS sensors equipped with “rolling shutters”, which distinguishes different lines per frame to become light sensitive at different moments in time, and stylized dithering of image acquisition (IMAQ) time caused by different computer programs running in the background will greatly influence the accuracy of the measured HR. In this paper, we analyze the phase error caused by CMOS sensor and the system error introduced by system sampling clock jitters. According to derivation, we propose two methods, amplitude-frequency superposition and a cubic spline interpolation reconstruction method based on actual schedules, that can be widely utilized in computer vision to overcome the camera phase error and sampling time fluctuation error. Amplitude of signal is analyzed and processed in amplitude-frequency domain in the method of amplitude-frequency superposition, which ignores the signal phase. Thus it can eliminate the phase error effectively. The cubic spline interpolation reconstruction method based on actual schedules can reconstructed the non-uniform sampling of images as uniform ones, so it can eliminate the system error involved by the system clock jitters. What’s more, the properties of the methods are tested by applying them to both simulation experiments and real HR measurements. In the simulation, amplitude of measured signal is improved 458% relative to the amplitude measured without the method of amplitude-frequency superposition; root mean square error of signal’s frequency, detected by the cubic spline interpolation reconstruction method based on actual schedules, is reduced more than 30%. In the real HR measurements, the amplitude of HR is raised to 335% relatively based on amplitude-frequency superposition. And the accuracy of HR is raised to approximately 40% by the method of cubic spline interpolation reconstruction method based on actual schedules. Therefore, the simulation experiments and real HR measurement proof that we can effectively eliminate the camera phase error based on the amplitude-frequency superposition extraction method, and the cubic spline interpolation based on the timetable method can effectively reduce the random error in IMAQ due to system clock jitters. These methods can both be widely used in dynamic signal detection based on machine vision.