Lee Ching-Long, Pei Wen, Lin Yu-Cheng, Granmo Anders, Liu Kang-Hung
Ph.D. Program of Management, Chung Hua University, Hsinchu 300, Taiwan.
Department of Business Administration, Chung Hua University, Hsinchu 300, Taiwan.
Healthcare (Basel). 2023 Jan 21;11(3):322. doi: 10.3390/healthcare11030322.
Emotion detection is a fundamental component in the field of Affective Computing. Proper recognition of emotions can be useful in improving the interaction between humans and machines, for instance, with regard to designing effective user interfaces. This study aims to understand the relationship between emotion and pupil dilation. The Tobii Pro X3-120 eye tracker was used to collect pupillary responses from 30 participants exposed to content designed to evoke specific emotions. Six different video scenarios were selected and presented to participants, whose pupillary responses were measured while watching the material. In total, 16 data features (8 features per eye) were extracted from the pupillary response distribution during content exposure. Through logistical regression, a maximum of 76% classification accuracy was obtained through the measurement of pupillary response in predicting emotions classified as fear, anger, or surprise. Further research is required to precisely calculate pupil size variations in relation to emotionally evocative input in affective computing applications.
情感检测是情感计算领域的一个基本组成部分。正确识别情感有助于改善人机交互，例如在设计有效的用户界面方面。本研究旨在了解情感与瞳孔扩张之间的关系。使用托比Pro X3-120眼动仪收集30名参与者在接触旨在引发特定情感的内容时的瞳孔反应。选择了六种不同的视频场景并呈现给参与者，在他们观看材料时测量其瞳孔反应。在内容呈现期间，从瞳孔反应分布中总共提取了16个数据特征（每只眼睛8个特征）。通过逻辑回归，通过测量瞳孔反应来预测被分类为恐惧、愤怒或惊讶的情感时，获得了高达76%的分类准确率。在情感计算应用中，需要进一步研究以精确计算与情感唤起输入相关的瞳孔大小变化。
