MohsenMozaffary / Deep_Respiration

This repository contains the codes for deep learning-based remote respiration rate estimation.

Deep_Respiration

This repository contains implementations of four different deep learning-based algorithms for remote respiration rate estimation from thermal camera recordings: PhsyNet, DeepPhys, StressNet, and PhysLSTM. These algorithms leverage the power of deep learning techniques implemented using PyTorch.

Algorithms Implemented

PhsyNet: A deep learning-based algorithm for respiration rate estimation from thermal imagery. DeepPhys: Another deep learning approach tailored for remote respiration rate estimation using thermal camera recordings. StressNet: A method designed specifically for stress detection from physiological signals including respiration rate obtained from thermal imaging. PhysLSTM: A novel deep learning-based algorithm developed as part of this project, utilizing Long Short-Term Memory (LSTM) networks for respiration rate estimation from thermal camera recordings.

Requirements

PyTorch
NumPy
OpenCV (optional, for preprocessing)