K-EmoPhone, A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels
Creators
- 1. KAIST
- 2. Kangwon University
- 3. Khalifa University
Description
ABSTRACT: With the popularization of low-cost mobile and wearable sensors, prior studies have utilized such sensors to track and analyze people's mental well-being, productivity, and behavioral patterns. However, there still is a lack of open datasets collected in-the-wild contexts with affective and cognitive state labels such as emotion, stress, and attention, which would limit the advances of research in affective computing and human-computer interaction. This work presents K-EmoPhone, an in-the-wild multi-modal dataset collected from 77 university students for seven days. This dataset contains (i) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices; (ii) context and interaction data collected from individuals' smartphones; and (iii) 5,582 self-reported affect states, such as emotion, stress, attention, and disturbance, acquired by the experience sampling method. We anticipate that the presented dataset will contribute to the advancement of affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.
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Last update: Apr. 12, 2023 ----------------------------- * Version 1.1.2 (Jun. 3, 2023)
* Version 1.1.1 (Apr. 12, 2023)
* Version 1.1.0 (Feb. 5, 2023)
* Version 1.0.0 (Aug. 3, 2022)
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Notes
1. This dataset is currently available at Scientific Data, Nature. The primary encoding of data files is "utf-8", but "cp949" may be needed due to Korean characters.
2. When applying for the dataset, please fill in the user agreement form as well (http://tiny.cc/kemo-eula).
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Additional details
References
- Kang, S., Choi, W., Park, C.Y. et al. K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels. Sci Data 10, 351 (2023). https://doi.org/10.1038/s41597-023-02248-2