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Deep learning (neural network) based remote photoplethysmography: how to extract pulse signal from video using deep learning tools

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Deep-rPPG

Deep learning (neural network) based remote photoplethysmography: how to extract pulse signal from video using deep learning tools

Implemented networks

DeepPhys

Chen, Weixuan, and Daniel McDuff. "Deepphys: Video-based physiological measurement using convolutional attention networks." Proceedings of the European Conference on Computer Vision (ECCV). 2018.

PhysNet

Yu, Zitong, Xiaobai Li, and Guoying Zhao. "Remote photoplethysmograph signal measurement from facial videos using spatio-temporal networks." Proc. BMVC. 2019.

Special application on neonates

A custom YOLO network is used to crop the baby as a preprocessing step. This network was created based on this repo: https://github.com/eriklindernoren/PyTorch-YOLOv3

Our modified version: https://github.com/terbed/PyTorch-YOLOv3

NVIDIA Jetson Nano inference

The running speed of the networks are tested on NVIDIA Jetson Nano. Results and the installation steps of PyTorch and OpenCV are in the nano folder.

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Deep learning (neural network) based remote photoplethysmography: how to extract pulse signal from video using deep learning tools

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  • Python 93.7%
  • Shell 6.3%