ArticleScientific data2020
K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- MindTS-MMD: A Chinese Multimodal Dataset for Emotion Expression Analysis in Children with Tourette Syndrome.Scientific data · 2026Article
- A multimodal dataset for emotional transition analysis in virtual reality.Scientific data · 2026Article
- A dataset on the experimental study of online and offline communication with digital and biomarkers.Scientific data · 2026Article
- The Brain, Body, and Behavior Dataset (BBBD): Multimodal Recordings during Educational Videos.Scientific data · 2026Article
- Bidirectional coupling of physiology and emotions reveals unique speaker-listener dynamics.Social cognitive and affective neuroscience · 2026Article
- A Multimodal Dataset for Assessing Emotion, Stress, and Emotional Workload in Interpersonal Work Scenario.Scientific data · 2026Article
- Continuous bidirectional coupling of heart rate variability and emotions in dyad speaker-listener dynamics reveals unique interpersonal synchronization.Frontiers in systems neuroscience · 2026Article
- Spatiotemporal multimodal emotion recognition using Temporal video sequences and pose features for child emotion classification.Scientific reports · 2025Article
- Stress and Emotion Open Access Data: A Review on Datasets, Modalities, Methods, Challenges, and Future Research Perspectives.Journal of healthcare informatics research · 2025Review
- A Comprehensive Review of Multimodal Emotion Recognition: Techniques, Challenges, and Future Directions.Biomimetics (Basel, Switzerland) · 2025Review
- Incorporating Multimodal Directional Interpersonal Synchrony into Empathetic Response Generation.Sensors (Basel, Switzerland) · 2025Article
- The competitive esports physiological, affective, and video dataset.Scientific data · 2025Article
- A Review on Deep Learning for Quality of Life Assessment Through the Use of Wearable Data.IEEE open journal of engineering in medicine and biology · 2025Article
- GameVibe: a multimodal affective game corpus.Scientific data · 2024Article
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts.Scientific data · 2024Article
- A Comprehensive Review of Hardware Acceleration Techniques and Convolutional Neural Networks for EEG Signals.Sensors (Basel, Switzerland) · 2024Review
- A Multimodal Dataset for Mixed Emotion Recognition.Scientific data · 2024Article
- Review
- Article
- Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set.Scientific data · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
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.
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