K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 32
  • Download : 1
Recognizing emotions during social interactions has many potential applications with the popularization of low-cost mobile sensors, but a challenge remains with the lack of naturalistic affective interaction data. Most existing emotion datasets do not support studying idiosyncratic emotions arising in the wild as they were collected in constrained environments. Therefore, studying emotions in the context of social interactions requires a novel dataset, and K-EmoCon is such a multimodal dataset with comprehensive annotations of continuous emotions during naturalistic conversations. The dataset contains multimodal measurements, including audiovisual recordings, EEG, and peripheral physiological signals, acquired with off-the-shelf devices from 16 sessions of approximately 10-minute long paired debates on a social issue. Distinct from previous datasets, it includes emotion annotations from all three available perspectives: self, debate partner, and external observers. Raters annotated emotional displays at intervals of every 5 seconds while viewing the debate footage, in terms of arousal-valence and 18 additional categorical emotions. The resulting K-EmoCon is the first publicly available emotion dataset accommodating the multiperspective assessment of emotions during social interactions.
Publisher
NATURE RESEARCH
Issue Date
2020-09
Language
English
Article Type
Article; Data Paper
Citation

SCIENTIFIC DATA, v.7, no.1, pp.293

ISSN
2052-4463
DOI
10.1038/s41597-020-00630-y
URI
http://hdl.handle.net/10203/276823
Appears in Collection
CS-Journal Papers(저널논문)BiS-Journal Papers(저널논문)
Files in This Item
000571812600006.pdf(3.53 MB)Download

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0