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Code for the paper "Seamless Monitoring of Stress Levels Leveraging a Universal Model for Time Sequences".

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Wearable Abnormal Emotion Detection

This repository contains the code for the paper "Seamless Monitoring of Stress Levels Leveraging a Universal Model for Time Sequences".

Setup

The following command installs the required dependencies:

# Install the dependencies
pip install -r requirements.txt

Datasets and Preprocessing

The following datasets are used in this project:

The datasets are preprocessed using the following steps:

# Download and extract the datasets
./data/download-datasets.sh

# Preprocess the datasets
python preprocess.py

Training

Ensure to be logged and to set your WANDB_USER in the file run.py to log the results to Weights & Biases. The following command launches the training of the models:

# Train the models
python run.py

Evaluation

The evaluation results are processed from logged runs on Weights & Biases. To retrieve the evaluation results, run the following command:

# Evaluate the models
python results.py

Acknowledgements

This project use the code from the repositiories of UniTS, TranAD and HypAD for the implementation of the models.

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Code for the paper "Seamless Monitoring of Stress Levels Leveraging a Universal Model for Time Sequences".

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