基于联合对角化算法的非接触心率估计

Non-contact Heart Rate Estimation Based on Joint Approximate Diagonalization of Eigenmatrices Algorithm

基于成像光电容积脉搏波描记法(iPPG)和盲源分离(BSS)理论,提出一种非接触估计心率方法。利用网络摄像头在自然光中录制人脸视频图像,将视频图像中检测的人脸区域分离为RGB三通道分量,对一定数量RGB数据做归一化、球化等预处理,再经独立分量分析(ICA)理论和联合对角化(JADE)算法,利用频谱分析估计心率。利用Bland-Altman理论分析估计结果与商用脉搏血氧仪测试结果的一致性,估计结果中均方根误差为2.06次/min,表明本方法能够实现非接触心率测量,同时可以作为远程、非接触多生理参数测量的研究基础。

Based on the imaging photoplethysmography （iPPG） and blind source separation （BSS） theory the author put forward a method for non-contact heartbeat frequency estimation. Using the recorded video images of the human face in the ambient light with Webcam, we detected the human face through software, separated the detected facial image into three channels RGB components. And then preprocesses i. e. normalization, whitening, etc. were carried out to a certain number of RGB data. After the independent component analysis （ICA） theory and joint approximate diagonalization of eigenmatrices （JADE） algorithm were applied, we estimated the frequency of heart rate through spectrum analysis. Taking advantage of the consistency of BIand-Altman theory analysis and the commercial Pulse Oximetry Sensor test results, the root mean square error of the algorithm result was calculated as 2.06 beat/rain. It indicated that the algorithm could realize the non-contact measurement of heart rate and lay the foundation for the remote and non-contact measurement of multi-parameter physiological measurements.