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.
Driving can occupy a considerable part of our daily lives and is often associated with high levels of stress. Motivated by the effectiveness of controlled breathing, this work studies the potential use of breathing interventions while driving to help manage stress. In particular, we implemented and evaluated a closed-loop system that monitored the breathing rate of drivers in real-time and delivered either a conscious or an unconscious personalized acoustic breathing guide whenever needed. In a study with 24 participants, we observed that conscious interventions more effectively reduced the breathing rate but also increased the number of driving mistakes. We observed that prior driving experience as well as personality are significantly associated with the effect of the interventions, which highlights the importance of considering user profiles for in-car stress management interventions. CCS CONCEPTS • Human-centered computing → Auditory feedback; Empirical studies in HCI ; • Applied computing → Consumer health.
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