The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset
- Submitting institution
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The University of East Anglia
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 182621879
- Type
- D - Journal article
- DOI
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10.1109/TAFFC.2015.2462830
- Title of journal
- IEEE Transactions on Affective Computing
- Article number
- -
- First page
- 435
- Volume
- 7
- Issue
- 4
- ISSN
- 1949-3045
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2016
- URL
-
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- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
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17
- Research group(s)
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-
- Citation count
- 31
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper describes the collection of and sets the analytical benchmark on a multimodal dataset of human behaviour in Chronic Pain (CP). The dataset is publicly available with permission. It is of interest to communities in computing and engineering as it contains: tracked face expression imagery, full body motion capture data, electromyographic signals as well as qualitative behaviour labels annotated by experts. It is also of interest to physiotherapy, psychology and medical communities. The paper also sets a benchmark for predictive modelling of clinically relevant behaviour. The dataset forms the basis of the international machine learning competition in 2020 (https://mvrjustid.github.io/EmoPainChallenge2020/).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -