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Computer Science > Human-Computer Interaction

arXiv:2509.15774 (cs)
[Submitted on 19 Sep 2025 ]

Title: Affective Air Quality Dataset: Personal Chemical Emissions from Emotional Videos

Title: 情感空气质量数据集:来自情绪视频的个人化学排放

Authors:Jas Brooks, Javier Hernandez, Mary Czerwinski, Judith Amores
Abstract: Inspired by the role of chemosignals in conveying emotional states, this paper introduces the Affective Air Quality (AAQ) dataset, a novel dataset collected to explore the potential of volatile odor compound and gas sensor data for non-contact emotion detection. This dataset bridges the gap between the realms of breath \& body odor emission (personal chemical emissions) analysis and established practices in affective computing. Comprising 4-channel gas sensor data from 23 participants at two distances from the body (wearable and desktop), alongside emotional ratings elicited by targeted movie clips, the dataset encapsulates initial groundwork to analyze the correlation between personal chemical emissions and varied emotional responses. The AAQ dataset also provides insights drawn from exit interviews, thereby painting a holistic picture of perceptions regarding air quality monitoring and its implications for privacy. By offering this dataset alongside preliminary attempts at emotion recognition models based on it to the broader research community, we seek to advance the development of odor-based affect recognition models that prioritize user privacy and comfort.
Abstract: 受化学信号在传达情绪状态中的作用启发,本文介绍了情感空气质量(AAQ)数据集,这是一个新的数据集,旨在探索挥发性气味化合物和气体传感器数据在非接触式情绪检测中的潜力。 该数据集弥合了呼吸与体味排放(个人化学排放)分析领域与情感计算中现有实践之间的差距。 该数据集包含23名参与者在距离身体不同位置(可穿戴和桌面)的4通道气体传感器数据,以及由针对性电影片段引发的情绪评分,该数据集涵盖了分析个人化学排放与不同情绪反应之间相关性的初步基础。 AAQ数据集还提供了来自退出访谈的见解,从而描绘了对空气质量监测及其对隐私影响的全面看法。 通过将此数据集以及基于它的初步情绪识别模型提供给更广泛的研究社区,我们旨在推动以用户隐私和舒适度为重点的基于气味的情感识别模型的发展。
Comments: 11 pages, 4 figures
Subjects: Human-Computer Interaction (cs.HC) ; Emerging Technologies (cs.ET)
Cite as: arXiv:2509.15774 [cs.HC]
  (or arXiv:2509.15774v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2509.15774
arXiv-issued DOI via DataCite

Submission history

From: Jas Brooks [view email]
[v1] Fri, 19 Sep 2025 09:01:54 UTC (1,859 KB)
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