Automating the Development of Stress Detection Systems
- Resource Type
- Conference
- Authors
- Beierle, Felix; Pryss, Rudiger
- Source
- 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) CSCE Computer Science, Computer Engineering, & Applied Computing (CSCE), 2023 Congress in. :2694-2696 Jul, 2023
- Subject
- Computing and Processing
Economics
Automation
Machine learning
Real-time systems
Libraries
Stress
Wearable sensors
stress detection
machine learning
AutoML
time series data
wearables
feature engineering
- Language
Stress is leading to bad health and contributes to economic loss due to employee absence. Real-time stress detection based on wearable sensor data can enable the implementation of mitigating strategies. While several approaches to stress detection exist, setting up a new system can be tedious. We demonstrate how the use of libraries and tools for automation can speed up many of the necessary steps when developing a stress detection system. We employ automated feature engineering and automated machine learning. The resulting stress detection system we developed this way is based on the WESAD dataset and achieves a F1 score of 0.87 for unseen users based on 30 seconds of wearable sensor data.