School of Mechanical Engineering, Southeast University, 2 Southeast University Road, Nanjing 211189, China.
School of Instrument Science and Engineering, Southeast University, 2 Southeast University Road, Nanjing 211189, China.
Sensors (Basel). 2023 Sep 20;23(18):7991. doi: 10.3390/s23187991.
The acquisition of physiological signals for analyzing emotional experiences has been intrusive, and potentially yields inaccurate results. This study employed infrared thermal images (IRTIs), a noninvasive technique, to classify user emotional experiences while interacting with business-to-consumer (B2C) websites. By manipulating the usability and aesthetics of B2C websites, the facial thermal images of 24 participants were captured as they engaged with the different websites. Machine learning techniques were leveraged to classify their emotional experiences, with participants' self-assessments serving as the ground truth. The findings revealed significant fluctuations in emotional valence, while the participants' arousal levels remained consistent, enabling the categorization of emotional experiences into positive and negative states. The support vector machine (SVM) model performed well in distinguishing between baseline and emotional experiences. Furthermore, this study identified key regions of interest (ROIs) and effective classification features in machine learning. These findings not only established a significant connection between user emotional experiences and IRTIs but also broadened the research perspective on the utility of IRTIs in the field of emotion analysis.
获取用于分析情感体验的生理信号一直具有侵入性，并且可能产生不准确的结果。本研究采用了非侵入性技术——红外热成像（IRTIs），来对用户在与企业对消费者（B2C）网站交互时的情感体验进行分类。通过操纵 B2C 网站的可用性和美观性，当 24 名参与者与不同的网站进行交互时，我们捕捉了他们的面部热图像。我们利用机器学习技术对他们的情感体验进行分类，参与者的自我评估作为基准。研究结果显示，情感效价有显著波动，而参与者的唤醒水平保持一致，从而能够将情感体验分为积极和消极状态。支持向量机（SVM）模型在区分基线和情感体验方面表现良好。此外，本研究还确定了机器学习中感兴趣的关键区域（ROIs）和有效分类特征。这些发现不仅在用户情感体验和 IRTIs 之间建立了重要联系，还拓宽了 IRTIs 在情感分析领域的应用研究视角。
