Rukavina Stefanie, Gruss Sascha, Hoffmann Holger, Tan Jun-Wen, Walter Steffen, Traue Harald C
Department of Psychosomatic Medicine and Psychotherapy, Medical Psychology, Ulm University, Ulm, Germany.
College of Teacher Education, Lishui University, Lishui, P.R. China.
PLoS One. 2016 Mar 3;11(3):e0150584. doi: 10.1371/journal.pone.0150584. eCollection 2016.
Affective computing aims at the detection of users' mental states, in particular, emotions and dispositions during human-computer interactions. Detection can be achieved by measuring multimodal signals, namely, speech, facial expressions and/or psychobiology. Over the past years, one major approach was to identify the best features for each signal using different classification methods. Although this is of high priority, other subject-specific variables should not be neglected. In our study, we analyzed the effect of gender, age, personality and gender roles on the extracted psychobiological features (derived from skin conductance level, facial electromyography and heart rate variability) as well as the influence on the classification results. In an experimental human-computer interaction, five different affective states with picture material from the International Affective Picture System and ULM pictures were induced. A total of 127 subjects participated in the study. Among all potentially influencing variables (gender has been reported to be influential), age was the only variable that correlated significantly with psychobiological responses. In summary, the conducted classification processes resulted in 20% classification accuracy differences according to age and gender, especially when comparing the neutral condition with four other affective states. We suggest taking age and gender specifically into account for future studies in affective computing, as these may lead to an improvement of emotion recognition accuracy.
情感计算旨在检测用户在人机交互过程中的心理状态，尤其是情绪和性情。检测可以通过测量多模态信号来实现，即语音、面部表情和/或心理生物学信号。在过去几年中，一种主要方法是使用不同的分类方法为每个信号识别最佳特征。尽管这是高度优先事项，但其他特定于主体的变量也不应被忽视。在我们的研究中，我们分析了性别、年龄、个性和性别角色对提取的心理生物学特征（源自皮肤电导率水平、面部肌电图和心率变异性）的影响以及对分类结果的影响。在一个实验性人机交互中，使用来自国际情感图片系统和乌尔姆大学图片的图片材料诱导出五种不同的情感状态。共有127名受试者参与了该研究。在所有潜在影响变量中（据报道性别有影响），年龄是唯一与心理生物学反应显著相关的变量。总之，所进行的分类过程根据年龄和性别导致了20%的分类准确率差异，特别是在将中性状态与其他四种情感状态进行比较时。我们建议在未来的情感计算研究中特别考虑年龄和性别，因为这可能会提高情绪识别准确率。
