ArticleScientific data2022
Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An Overview of Stress Analysis Based on Physiological Signals: Systematic Review of Open Datasets and Current Trends.Sensors (Basel, Switzerland) · 2025Pooled it
- An emotion recognition dataset using millimeter wave radar and physiological reference signals.Scientific data · 2026Article
- A Multimodal Dataset for Assessing Emotion, Stress, and Emotional Workload in Interpersonal Work Scenario.Scientific data · 2026Article
- Virtual reality-induced emotion recognition with deep learning-based multimodal physiological feature fusion.Frontiers in psychology · 2026Article
- An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition.Scientific data · 2025Article
- LLaMAC: low-cost biosignal sensor based large multimodal dataset for affective computing.Scientific data · 2025Article
- Emotion Recognition Using Temporal Facial Skin Temperature and Eye-Opening Degree During Digital Content Viewing for Japanese Older Adults.Sensors (Basel, Switzerland) · 2025Article
- Towards Fault-Aware Image Captioning: A Review on Integrating Facial Expression Recognition (FER) and Object Detection.Sensors (Basel, Switzerland) · 2025Review
- Fatigue and stamina prediction of athletic person on track using thermal facial biomarkers and optimized machine learning algorithm.Scientific reports · 2025Article
- A Review of 25 Spontaneous and Dynamic Facial Expression Databases of Basic Emotions.Affective science · 2025Review
- An ensemble deep learning framework for emotion recognition through wearable devices multi-modal physiological signals.Scientific reports · 2025Article
- Travel experience in public transport: Experience sampling and cardiac activity data for spatial analysis.Scientific data · 2025Article
- The competitive esports physiological, affective, and video dataset.Scientific data · 2025Article
- Naturalistic multimodal emotion data with deep learning can advance the theoretical understanding of emotion.Psychological research · 2024Review
- A Multimodal Dataset for Mixed Emotion Recognition.Scientific data · 2024Article
- Image-based facial emotion recognition using convolutional neural network on emognition dataset.Scientific reports · 2024Article
- Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set.Scientific data · 2024Article
- Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study.Applied sciences (Basel, Switzerland) · 2024Article
- Ethical considerations for integrating multimodal computer perception and neurotechnology.Frontiers in human neuroscience · 2024Article
- I DARE: IULM Dataset of Affective Responses.Frontiers in human neuroscience · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The Emognition dataset is dedicated to testing methods for emotion recognition (ER) from physiological responses and facial expressions. We collected data from 43 participants who watched short film clips eliciting nine discrete emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, and sadness. Three wearables were used to record physiological data: EEG, BVP (2x), HR, EDA, SKT, ACC (3x), and GYRO (2x); in parallel with the upper-body videos. After each film clip, participants completed two types of self-reports: (1) related to nine discrete emotions and (2) three affective dimensions: valence, arousal, and motivation. The obtained data facilitates various ER approaches, e.g., multimodal ER, EEG- vs. cardiovascular-based ER, discrete to dimensional representation transitions. The technical validation indicated that watching film clips elicited the targeted emotions. It also supported signals' high quality.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.