Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
Faculty of Information and Communication Studies, University of the Philippines Open University, Los Baños 4031, Philippines.
Sensors (Basel). 2023 Jul 25;23(15):6664. doi: 10.3390/s23156664.
Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.
可穿戴设备的现代进步激发了人们利用各种生理指标持续观察压力的兴趣。早期压力检测可以通过减轻慢性压力的负面影响来改善医疗保健。机器学习 (ML) 方法已经针对医疗设备进行了修改，以便利用足够的用户信息来监测用户的健康状况。然而，在医疗领域应用人工智能 (AI) 方法需要更多的数据。本研究旨在使用基于机器学习算法的堆叠模型来检测压力，该模型使用来自可穿戴压力和情感检测 (WESAD) 数据集的基于胸部的特征。我们通过使用 RESP 特征进行数据可视化和预处理，并使用 Z 分数、SelectKBest 特征、合成少数过采样技术 (SMOTE) 和归一化进行特征分析，将这个自然数据集转换为适合建议模型的格式。我们根据准确性、精度、召回率和 F1 分数来评估建议模型的效率。实验结果表明，所提出的堆叠技术具有很高的效率，准确率达到 0.99%。结果表明，所提出的堆叠方法比传统方法和先前的研究表现更好。
