Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, People's Republic of China.
Physiol Meas. 2019 Sep 3;40(8):085007. doi: 10.1088/1361-6579/ab2c9f.
Remote photoplethysmography (rPPG) can achieve non-contact measurement of heart rate (HR) from a continuous video sequence by scanning the skin surface. However, practical applications are still limited by factors such as non-rigid facial motion and head movement. In this work, a detailed system framework for remotely estimating heart rate from facial videos under various movement conditions is described.
After the rPPG signal has been obtained from a defined region of the facial skin, a method, termed 'Project_ICA', based on a skin reflection model, is employed to extract the pulse signal from the original signal.
To evaluate the performance of the proposed algorithm, a dataset containing 112 videos including the challenges of various skin tones, body motion and HR recovery after exercise was created from 28 participants.
The results show that Project_ICA, when evaluated by several criteria, provides a more accurate and robust estimate of HR than most existing methods, although problems remain in obtaining reliable measurements from dark-skinned subjects.
远程光电容积脉搏波描记术（rPPG）可以通过扫描皮肤表面，从连续的视频序列中实现心率（HR）的非接触式测量。然而，实际应用仍然受到非刚性面部运动和头部运动等因素的限制。在这项工作中，描述了一种用于在各种运动条件下从面部视频远程估计心率的详细系统框架。
在从面部皮肤的定义区域获得 rPPG 信号后，使用一种基于皮肤反射模型的方法“Project_ICA”从原始信号中提取脉搏信号。
为了评估所提出算法的性能，从 28 名参与者中创建了一个包含 112 个视频的数据集，这些视频包括各种肤色、身体运动和运动后心率恢复的挑战。
结果表明，尽管在从深色皮肤受试者中获得可靠测量值方面仍存在问题，但与大多数现有方法相比，Project_ICA 在通过多个标准评估时，提供了更准确和稳健的 HR 估计。
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