Automating the Development of Stress Detection Systems
2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE)(2023)
摘要
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.
更多查看译文
关键词
stress detection,machine learning,AutoML,time series data,wearables,feature engineering
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要