基于双摄像头的心率测量系统的设计与改进

针对在外部干扰或昏暗环境下利用图像光电容积描记(image Photoplethysmography,iPPG)技术进行心率测量时准确度较差,提出了一种自适应心率提取算法,并在嵌入式硬件平台上进行了验证。算法根据图像中人脸与背景区域的色度关系来识别不同的场景并启动合适的摄像头进行图像采集及自适应映射,接着对提取出的信号进行滤波,在信号质量评估后输出结果。上述方法在Zynq平台上进行了验证,使用双摄像头实现实时心率测量,并对结果进行可视化输出。实验结果表明:优化后的算法在光照及运动的双重干扰下的测量误差从3.36BPM降至2.78BPM,准确率提升了17.3%。另外,所设计的系统能够实现在极端黑暗条件下的心率采集,平均误差约为2.39BPM。

Design and Improvement of Heart Rate Measuring System Based on Dual Camera

In order to solve the problem that the accuracy of heart rate measurement is poor under external interference or dim environment by image Photoplethysmography (iPPG), an adaptive heart rate extraction algorithm was presented and was verified on an embedded hardware platform. Different scenes could be identified by this algorithm according to the chromaticity relationship between faces and the background areas in the image. Subsequently, the appropriate camera was launched for image acquisition and adaptive mapping. Finally, the extracted signal was filtered and the result was output after the signal quality evaluation. The algorithm was implemented on the embedded Zynq system with dual cameras for real-time heat rate measurement, and the results were visualized. Experimental results show that the measurement error decreases from 3.36BPM to 2.78BPM under the interference of light and motion, and the accuracy is improved by 17.3%. In addition, the designed system can also achieve heart rate acquisition under extreme dark conditions, with an average error of about 2.39BPM.