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+# 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