Feature Selection Framework for XGBoost Based on Electrodermal Activity in Stress Detection

Cheng-Ping Hsieh · Yi-Ta Chen · Win-Ken Beh · An-Yeu Andy Wu

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Abstract

Since stress has a strong influence on human's health, it is necessary to automatically detect stress in our daily life. In this paper, we aim to improve the performance and obtain the dominant features in stress detection based on Electrodermal Activity (EDA). Compared to the methods in Wearable Stress and Affect Dataset (WESAD), we propose several enhancements to get higher f1-scores, including less overlapped signal segmentation, more signal processing features, and extreme gradient boosting classification algorithm (XGBoost). Furthermore, we select dominant features according to their importance in classifier and correlation among other features while keeping high performance. Experiment results show that with 9 dominant features in XGBoost, we can achieve 92.38% (+ 17.87%) and 89.92% (+14.58%) f1-scores compared to WESAD on chest-and wrist-based EDA signal respectively. The features we choose suggest that the magnitude of low frequency and the complexity of high frequency EDA signal contain the most significant information in stress detection.

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References

2005Detecting Stress During Real-World Driving Tasks Using Physiological Sensors 2018Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection 2008Emotion recognition based on physiological changes in music listening 2009Discriminating Stress From Cognitive Load Using a Wearable EDA Device 2012StressSense 2015Automatic identification of artifacts in electrodermal activity data 2016Continuous stress detection using a wrist device 2013Stress detection from speech and Galvanic Skin Response signals

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2025Machine and Deep Learning Models for Stress Detection Using Multimodal Physiological Data 2024A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study 2024Optimization of Wearable Biosensor Data for Stress Classification Using Machine Learning and Explainable AI

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