Abstract
Non-contact physiological parameters detection has become an important but challenging task. This paper proposes a novel framework for Non-contact physiological parameters detection, which is based on MTCNN (Multi-task convolutional neural network) and EVM (Eulerian Video Magnification). The MTCNN is applied to compensate negative effects of the pseudo motion for improving the efficiency of face detection. The Laplacian pyramid of EVM is added to our framework to perform motion amplification to achieve simultaneous extraction of heart rate and respiration rate, which improves the accuracy of non-contact physiological parameter detection. Then we use FFT to analyze the relevant frequency bands in time domain and calculate the heart rate and breathing rate. In order to improve efficiency, GPU acceleration is implemented under the TensorRT framework. The experimental results based on data sets DUSS (detection under slight shaking) and DUO (detection under occlusion) show the effectiveness of this method. The average error of the heart rate and respiration rate are −0.1 and 0.6, respectively. Our method achieves comparable performance to finger clip oximeter with a good consistency, even in the case of wearing a mask.
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Acknowledgments
This work was supported by the Project (19YFZCSF01150) in the Science & Technology Pillar Program of Tianjin, in part by the Tianjin Natural Science Foundation of China under Grant 18JCZDJC40300, in part by Innovation cultivation Foundation 19-163-12-ZT-006-007-06.
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Zhao, J., Zhang, W., Chai, R., Wu, H., Chen, W. (2021). Non-contact Physiological Parameters Detection Based on MTCNN and EVM. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_48
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DOI: https://doi.org/10.1007/978-981-16-2336-3_48
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