EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions
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Abstract (arXiv)
The EmoPain 2020 Challenge is the first international competition aimed at creating a uniform platform for the comparison of machine learning and multimedia processing methods of automatic chronic pain assessment from human expressive behaviour, and also the identification of pain-related behaviours. The objective of the challenge is to promote research in the development of assistive technologies that help improve the quality of life for people with chronic pain via real-time monitoring and feedback to help manage their condition and remain physically active. The challenge also aims to encourage the use of the relatively underutilised, albeit vital bodily expression signals for automatic pain and pain-related emotion recognition. This paper presents a description of the challenge, competition guidelines, bench-marking dataset, and the baseline systems' architecture and performance on the three sub-tasks: pain estimation from facial expressions, pain recognition from multimodal movement, and protective movement behaviour detection.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 2; 2025: 2; 2026: 3.
11 papers.
- 2024UniTS: A Unified Multi-Task Time Series Modelcited by 93
- 2022MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wildcited by 65
- 2021The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stresscited by 27
- 2025Mantis: Lightweight Foundation Model for Time Series Classificationcited by 21
- 2025Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approachcited by 14
- 2026MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategiescited by 10
- 2023Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learningcited by 9
- 2026The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classificationcited by 3
- 2022PrivacyProber: Assessment and Detection of Soft-Biometric Privacy-Enhancing Techniquescited by 0
- 2024STAL: Spike Threshold Adaptive Learning Encoder for Classification of Pain-Related Biosignal Datacited by 0
- 2026Towards a Unified Generative Model for Scarce Time Series with Domain Expertscited by 0
- 2026Towards a Unified Generative Model for Scarce Time Series with Domain Experts13 Jun 2026
- 2026The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification20 Mar 2026
- 2026MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies19 Feb 2026
- 2025Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach26 May 2025
- 2025Mantis: Lightweight Foundation Model for Time Series Classification21 Feb 2025
- 2024STAL: Spike Threshold Adaptive Learning Encoder for Classification of Pain-Related Biosignal Data11 Jul 2024
- 2024UniTS: A Unified Multi-Task Time Series Model29 Feb 2024
- 2023Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learning15 May 2023
- 2022PrivacyProber: Assessment and Detection of Soft-Biometric Privacy-Enhancing Techniques16 Nov 2022
- 2022MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild1 Aug 2022
- 2021The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress14 Apr 2021