Automating the Development of Stress Detection Systems

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.