https://archive.ics.uci.edu/ml/datasets/WESAD+%28Wearable+Stress+and+Affect+Detection%29

Run scripts in the following order (can take a while) to prepare data and extract features from the latent layer of autoencoder model:

1. Preprocess and merge subject data
   Command:
   python merge_subj_data.py
   
   Input data path: 'data/WESAD/'
   Generates the following files in data folder:
   subj_merged_acc_w.pkl
   subj_merged_bvp_w.pkl
   subj_merged_eda_temp_w.pkl
   merged_chest_fltr.pkl
   
   
2. Create autoencoder model and extract latent features
   Command:
   python extract_ae_latent_features.py
   
   Input files:
   subj_merged_acc_w.pkl
   subj_merged_bvp_w.pkl
   subj_merged_eda_temp_w.pkl
   merged_chest_fltr.pkl
   

• Uses ae_feature_extractor.py to build and train autoencoder model and extract features.
  
• Save extracted features leaving one subject out into pickle files in features/train and features/test directories. The number in the filename indicates which subject was left out in each fold.
  
  

3. SVM_classifier.ipynb - Build SVM classifier that uses latent features extracted by autoencoder for three class classification of WESAD dataset: neutral, stress, and ammusement. Results analysis also included.
   
   
4. MLP_classifier.ipynb - Build MLP (Multi Layer Perceptron) classifier that uses latent features extracted by autoencoder for three class classification of WESAD dataset: neutral, stress, and ammusement. Results analysis also included.