Stress detection with encoding physiological signals and convolutional neural network

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Michela Quadrini, Antonino Capuccio, Denise Falcone, Sebastian Daberdaku, Alessandro Blanda, Luca Bellanova, Gianluca Gerard
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Abstract

Stress is a significant and growing phenomenon in the modern world that leads to numerous health problems. Robust and non-invasive method developments for early and accurate stress detection are crucial in enhancing people’s quality of life. Previous researches show that using machine learning approaches on physiological signals is a reliable stress predictor by achieving significant results. However, it requires determining features by hand. Such a selection is a challenge in this context since stress determines nonspecific human responses. This work overcomes such limitations by considering STREDWES, an approach for Stress Detection from Wearable Sensors Data. STREDWES encodes signal fragments of physiological signals into images and classifies them by a Convolutional Neural Network (CNN). This study aims to study several encoding methods, including the Gramian Angular Summation/Difference Field method and Markov Transition Field, to evaluate the best way to encode signals into images in this domain. Such a study is performed on the NEURO dataset. Moreover, we investigate the usefulness of STREDWES in real scenarios by considering the SWELL dataset and a personalized approach. Finally, we compare the proposed approach with its competitors by considering the WESAD dataset. It outperforms the others.

Abstract Image

利用生理信号编码和卷积神经网络进行压力检测
压力是现代社会中一个重要且日益增长的现象,会导致许多健康问题。开发可靠的非侵入性方法,用于早期准确检测压力,对于提高人们的生活质量至关重要。以往的研究表明,在生理信号上使用机器学习方法是一种可靠的压力预测方法,能取得显著效果。然而,这需要人工确定特征。在这种情况下,这种选择是一个挑战,因为压力决定了人类的非特异性反应。STREDWES 是一种从可穿戴传感器数据中进行压力检测的方法,这项研究通过考虑 STREDWES 克服了上述局限性。STREDWES 将生理信号的信号片段编码成图像,并通过卷积神经网络(CNN)对其进行分类。本研究旨在研究几种编码方法,包括格拉米安角相加/差分场法和马尔可夫转换场法,以评估在该领域将信号编码成图像的最佳方法。这项研究是在 NEURO 数据集上进行的。此外,我们还通过考虑 SWELL 数据集和个性化方法,研究了 STREDWES 在实际场景中的实用性。最后,我们通过 WESAD 数据集将所提出的方法与其竞争对手进行了比较。结果显示,该方法优于其他方法。
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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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