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Evaluation of physiological signals collected from the E4 wristband to detect the stress-levels in students while performing arithmetic tasks.

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Stress Detection using finetuning

Data Collection:

  • An experiment with 25 participants, using an Empatica E4 wristband to record their physiological signals and determine their cognitive stress levels.
  • The experimental design can be found on GitHub

Data pre-processing

  • e4_raw_data\preprocessing_e4.ipynb
    • preprocessing raw E4 data
  • pre-processed and segmented using a sliding window of the length of 30 seconds without overlap
  • EDA, BVP, TEMP, HR and 3-axis ACC data from Empatica E4 is used for analysis

Cross Validations

  • Kfold (classifiers\kfold)
  • Leave-One-Subject-Out (LOSO) (classifiers\loso)
  • fine-tuning on LOSO (classifiers\loso)

Models

  • FCN
  • ResNet
  • Transformers
  • LSTM

Dashboard

  • displays dynamic stress fluctuations by utilizing the insights gained from prediction outcomes upon user-specific data
  • The application includes a stress meter, which enables users to visually understand their stress levels

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References

  • Maciej Dzieżyc, Martin Gjoreski, Przemysław Kazienko, Stanisław Saganowski, and Matjaž Gams. Can we ditch feature engineering? end-to-end deep learning for affect recognition from physiological sensor data. Sensors, 20(22):6535, 2020

  • Zhiguang Wang, Weizhong Yan, and Tim Oates. Time series classification from scratch with deep neural networks: A strong baseline. In 2017 International joint conference on neural networks (IJCNN), pages 1578–1585. IEEE, 2017

  • Theodoros Ntakouris. https://keras.io/examples/timeseries/, 2021

  • Jürgen Schmidhuber, Sepp Hochreiter, et al. Long short-term memory. Neural Comput, 9(8):1735–1780, 1997

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Evaluation of physiological signals collected from the E4 wristband to detect the stress-levels in students while performing arithmetic tasks.

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