High-accuracy heart rate detection using multispectral IPPG technology combined with a deep learning algorithm

Image Photoplethysmography (IPPG) technology is a noncontact physiological parameter detection technology, which has been widely used in heart rate (HR) detection. However, traditional imaging devices still have issues such as narrower receiving spectral range and inferior motion detection performance. In this paper, we propose a HR detection method based on multi-spectral video. Our method combining multispectral imaging with IPPG technology provides more accurate physiological information. To realize real-time evaluation of HR directly from facial multispectral videos, we propose a new end-to-end neural network, namely IPPGResNet18. The IPPGResNet18 model was trained on the multispectral video dataset from which better results were achieved: MAE = 2.793, RMSE = 3.695, SD = 3.707, p = 0.304. The experimental results demonstrate a high accuracy of HR detection under motion state using this detection method. In respect of real-time monitoring of HR during movement, our method is obviously superior to the conventional technical solutions.