# VitaNet
VitaNet is a radio frequency based contactless approach that accurately estimates the PPG signal using radar for stationary participants.

## Dataset Setup
We have processed the data from 5 participants over 49 full nights preprocessed as Tensorflow Records (TFRs). Here are the steps to setup the dataset:

1- Download the TFRs from [here](https://drive.google.com/drive/u/5/folders/1LYGRFEe5IZOAwCyU1O-YaAj_ySCpPKNP). Please note that due to data limits for anonymous accounts, we have only uploaded a small subset of data. We will release the full dataset on publication of the paper.

2- Move the downloaded TFRs to `code/tfrs`.

Data is now set up!

## Prerequisites

1- Install `conda`.

2- Install all the relevant packages through the `.yml` file that we have provided:

```
  cd code
  conda install -f vitanet.yml
```

## Model Training
We have a provided a Makefile to run training and inference routines. For training:

```
  cd code
  make train
```

The default routine runs for 300 epochs, which may take a long time. To run a shorter training routine (50 epochs), run:

```
  make train.fast
```

## Inference
For inference:

```
  make inference
```


### Code Overview

`common` contains scripts to load data from TFRs.

`code/models/paper.best` contains the best VitaNet model that we were able to train.

`code/tfrs` contains the data from 5 participants over 49 full nights preprocessed as TFRs.

`code/utils` contains some utility functions.

`code/vitanet.py` contains all major model functionality.