M Khayyat Mashael, Munshi Raafat M, Alabduallah Bayan, Lamoudan Tarik, Ghith Ehab, Kim Tai-Hoon, A Abdelhamid Abdelaziz
Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia.
PLoS One. 2024 Dec 17;19(12):e0310776. doi: 10.1371/journal.pone.0310776. eCollection 2024.
Biometric stress monitoring has become a critical area of research in understanding and managing health problems resulting from stress. One of the fields that emerged in this area is biometric stress monitoring, which provides continuous or real-time information about different anxiety levels among people by analyzing physiological signals and behavioral data. In this paper, we propose a new approach based on the CapsNets model for continuously monitoring psychophysiological stress. In the new model, streams of biometric data, including physiological signals and behavioral patterns, are taken up for analysis. In testing using the Swell multiclass dataset, it performed with an accuracy of 92.76%. Further testing of the WESAD dataset reveals an even better accuracy at 96.76%. The accuracy obtained for binary classification of stress and no stress class is applied to the Swell dataset, where this model obtained an outstanding accuracy of 98.52% in this study and on WESAD, 99.82%. Comparative analysis with other state-of-the-art models underlines the superior performance; it achieves better results than all of its competitors. The developed model is then rigorously subjected to 5-fold cross-validation, which proved very significant and proved that the proposed model could be effective and efficient in biometric stress monitoring.
生物特征应激监测已成为理解和管理由压力导致的健康问题的关键研究领域。在这一领域中出现的一个方向是生物特征应激监测，它通过分析生理信号和行为数据，提供有关人们不同焦虑水平的连续或实时信息。在本文中，我们提出了一种基于胶囊网络（CapsNets）模型的新方法，用于持续监测心理生理应激。在新模型中，生物特征数据流，包括生理信号和行为模式，被用于分析。在使用Swell多类数据集进行测试时，其准确率为92.76%。对WESAD数据集的进一步测试显示准确率更高，达到了96.76%。将压力和无压力类别的二元分类所获得的准确率应用于Swell数据集，在本研究中该模型在此数据集上获得了98.52%的出色准确率，在WESAD数据集上则为99.82%。与其他先进模型的对比分析突出了其卓越性能；它比所有竞争对手都取得了更好的结果。然后，对所开发的模型进行了严格的五折交叉验证，结果证明非常显著，表明所提出的模型在生物特征应激监测中可能是有效且高效的。
