K-EmoPhone, A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

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.

Last update: Apr. 12, 2023

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* Version 1.1.2 (Jun. 3, 2023)

Published the dataset at Scientific Data Journal.

Updated end-user license agreement.

* Version 1.1.1 (Apr. 12, 2023)

Updated file description and abstract.

* Version 1.1.0 (Feb. 5, 2023)

Updated the quality of the sensor data information. 

Deleted three participants (P27, P59, P65) due to the low quality issue.

* Version 1.0.0 (Aug. 3, 2022)

Added P##.zip files, where each P## means the separate participant.

Added SubjData.zip file, which includes individual characteristics information and labels.