基于视频的心率测量算法研究

随着心脏的收缩舒张，人脸部毛细血管中氧合血红蛋白的含量会发生变化。由于氧合血红蛋白对光线有一定的吸收作用，皮肤颜色也会随之发生轻微变化，这种变化裸眼无法识别。利用一种有效的视频增强方法，提出了一种基于视频的心率测量算法。该算法对输入视频进行人脸定位与特征点跟踪，将特征点区域转换为时域信号，对信号进行放大滤波处理，得到心率值。为了提高算法的测量精度和鲁棒性，提出了利用绿色通道、多特征点同时处理。同传统的接触式心率测量设备相比，该算法无需接触人体，有着对人体束缚度小、方便使用的优点。同时，该心率测量算法可实时运行，平均误差仅为3.46次/min，达到实际应用需求。

Video-based heart rate measuring method

The content of oxyhemoglobin in human facial capillaries changes with the contraction and relaxation of the heart. With oxyhemoglobin absorption of light, skin color will also vary slightly, which cannot be observed by naked eyes. Using an effective video enhancement algorithm, a video-based heart rate measuring method is proposed. The face in the input video is located and feature points are tracked. Then the regions of the feature points are transformed into time-domain signals. The signals are enlarged and filtered to obtain the heart rate. In order to improve the accuracy and robustness of the detection algorithm, the green channel and multi-feature points are utilized. Compared with the traditional contact heart rate measurement equipment, the algorithm does not require contact with the body and has the advantages of a small degree of human bondage. Meanwhile, the heart rate measurement algorithm can be performed in real time and meets the applicable requirement with an average error of only 3.46 time/minute.