Publication

Stress Detection by Machine Learning and Wearable Sensors

Prerna Garg, Jayasankar Santhosh, Andreas Dengel, Shoya Ishimaru

In: 26th International Conference on Intelligent User Interfaces - Companion. International Conference on Intelligent User Interfaces (IUI) New York, NY, USA IUI '21 Companion ISBN 9781450380188 Association for Computing Machinery 2021.

Abstract

Mental states like stress, depression, and anxiety have become a huge problem in our modern society. The main objective of this work is to detect stress among people, using Machine Learning approaches with the final aim of improving their quality of life. We propose various Machine Learning models for the detection of stress on individuals using a publicly available multimodal dataset, WESAD. Sensor data including electrocardiogram (ECG), body temperature (TEMP), respiration (RESP), electromyogram (EMG), and electrodermal activity (EDA) are taken for three physiological conditions - neutral (baseline), stress and amusement. The F1-score and accuracy for three-class (amusement vs. baseline vs. stress) and binary (stress vs. non-stress) classifications were computed and compared using machine learning techniques like k-NN, Linear Discriminant Analysis, Random Forest, AdaBoost, and Support Vector Machine. For both binary classification and three-class classification, the Random Forest model outperformed other models with F1-scores of 83.34 and 65.73 respectively.

Projekte

Weitere Links

Stress_Detection_by_Machine_Learning_and_Wearable_Sensors.pdf (pdf, 716 KB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz