Yamauchi Takashi, Xiao Kunchen
Department of Psychological and Brain Science, Texas A&M University.
Cogn Sci. 2018 Apr;42(3):771-819. doi: 10.1111/cogs.12557. Epub 2017 Nov 13.
Affective computing research has advanced emotion recognition systems using facial expressions, voices, gaits, and physiological signals, yet these methods are often impractical. This study integrates mouse cursor motion analysis into affective computing and investigates the idea that movements of the computer cursor can provide information about emotion of the computer user. We extracted 16-26 trajectory features during a choice-reaching task and examined the link between emotion and cursor motions. Participants were induced for positive or negative emotions by music, film clips, or emotional pictures, and they indicated their emotions with questionnaires. Our 10-fold cross-validation analysis shows that statistical models formed from "known" participants (training data) could predict nearly 10%-20% of the variance of positive affect and attentiveness ratings of "unknown" participants, suggesting that cursor movement patterns such as the area under curve and direction change help infer emotions of computer users.
情感计算研究已经推进了利用面部表情、声音、步态和生理信号的情感识别系统，但这些方法往往不切实际。本研究将鼠标光标运动分析整合到情感计算中，并探讨计算机光标的运动可以提供有关计算机用户情绪信息的观点。我们在一个选择到达任务中提取了16 - 26个轨迹特征，并研究了情绪与光标运动之间的联系。通过音乐、电影片段或情感图片诱导参与者产生积极或消极情绪，他们通过问卷表明自己的情绪。我们的10折交叉验证分析表明，由“已知”参与者（训练数据）形成的统计模型可以预测“未知”参与者近10% - 20%的积极情感和注意力评分方差，这表明诸如曲线下面积和方向变化等光标运动模式有助于推断计算机用户的情绪。
