计算机科学
愤怒
背景(考古学)
唤醒
价(化学)
领域(数学)
情绪识别
情绪分类
数据科学
人机交互
认知心理学
人工智能
心理学
社会心理学
古生物学
纯数学
物理
生物
量子力学
数学
作者
Sebastian Zepf,Javier Hernández,Alexander Schmitt,Wolfgang Minker,Rosalind W. Picard
摘要
Driving can occupy a large portion of daily life and often can elicit negative emotional states like anger or stress, which can significantly impact road safety and long-term human health. In recent decades, the arrival of new tools to help recognize human affect has inspired increasing interest in how to develop emotion-aware systems for cars. To help researchers make needed advances in this area, this article provides a comprehensive literature survey of work addressing the problem of human emotion recognition in an automotive context. We systematically review the literature back to 2002 and identify 63 peer-review published articles on this topic. We overview each study’s methodology to measure and recognize emotions in the context of driving. Across the literature, we find a strong preference toward studying emotional states associated with high arousal and negative valence, monitoring the different states with cardiac, electrodermal activity, and speech signals, and using supervised machine learning to automatically infer the underlying human affective states. This article summarizes the existing work together with publicly available resources (e.g., datasets and tools) to help new researchers get started in this field. We also identify new research opportunities to help advance progress for improving driver emotion recognition.
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