# Stress and Affect detection using WESAD dataset

Stress and Affect detection using WESAD dataset

Using **WEarable Stress and Affect Detection** (WESAD) dataset, training machine learning models such as Gaussian Mixture Model classifier,

Random forests to classify Stress vs. Non-stress and also Stress vs. Neutral vs. Amusement. 

Binary classification problem: **Stress vs. Non-stress** 

Logistic regression can be used to approximate non linear decision boundary.

3 class classification problem: **Stress vs. Neutral vs. Amusement**

Clustering using GMM and classifying using GMM classifier, 

Training Random forests for classification

Different modalities: **Chest vs. Wrist**

Evaluating the performance of the classifier based on different modalities

This dataset and idea is from the paper below:

Philip Schmidt, Attila Reiss, Robert Duerichen, Claus Marberger, Kristof Van Laerhoven, 

"Introducing WESAD, a multimodal dataset for Wearable Stress and Affect Detection", ICMI 2018, Boulder, USA, 2018