Stress Detection by Machine Learning and Wearable Sensors

Prerna Garg, Jayasankar Santhosh, A. Dengel, Shoya Ishimaru
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引用次数: 34

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.
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基于机器学习和可穿戴传感器的应力检测
精神状态,如压力、抑郁和焦虑已经成为我们现代社会的一个巨大问题。这项工作的主要目标是利用机器学习方法检测人们的压力,最终目的是提高他们的生活质量。我们提出了各种机器学习模型,用于使用公开可用的多模态数据集WESAD来检测个体的压力。传感器数据包括心电图(ECG)、体温(TEMP)、呼吸(RESP)、肌电图(EMG)和皮电活动(EDA)三种生理状态——中性(基线)、应激和娱乐。使用机器学习技术,如k-NN、线性判别分析、随机森林、AdaBoost和支持向量机,计算并比较了三级(娱乐、基线、压力)和二元(压力、非压力)分类的f1分数和准确性。对于二元分类和三类分类,随机森林模型的f1得分分别为83.34分和65.73分,优于其他模型。
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