ArticlePain research & management2022
Automatic Coding of Facial Expressions of Pain: Are We There Yet?
Article in Pain research & management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 28 citations in OpenAlex.
- Pain in the Context of Virtual Neuropsychological Assessment of Older Adults.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2024Article
- Potential use of salivary TNF-α as a vaccine-induced pain biomarker in people with cerebral palsy and communication disorders.PloS one · 2024Article
- Review
- Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches.Frontiers in pain research (Lausanne, Switzerland) · 2022Review
- Classification of elderly pain severity from automated video clip facial action unit analysis: A study from a Thai data repository.Frontiers in artificial intelligence · 2022Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe experience of pain is regularly accompanied by facial expressions. The gold standard for analyzing these facial expressions is the Facial Action Coding System (FACS), which provides so-called action units (AUs) as parametrical indicators of facial muscular activity. Particular combinations of AUs have appeared to be pain-indicative. The manual coding of AUs is, however, too time- and labor-intensive in clinical practice. New developments in automatic facial expression analysis have promised to enable automatic detection of AUs, which might be used for pain detection.
objectiveOur aim is to compare manual with automatic AU coding of facial expressions of pain.
methodsFaceReader7 was used for automatic AU detection. We compared the performance of FaceReader7 using videos of 40 participants (20 younger with a mean age of 25.7 years and 20 older with a mean age of 52.1 years) undergoing experimentally induced heat pain to manually coded AUs as gold standard labeling. Percentages of correctly and falsely classified AUs were calculated, and we computed as indicators of congruency, "sensitivity/recall," "precision," and "overall agreement (F1)."
resultsThe automatic coding of AUs only showed poor to moderate outcomes regarding sensitivity/recall, precision, and F1. The congruency was better for younger compared to older faces and was better for pain-indicative AUs compared to other AUs.
conclusionAt the moment, automatic analyses of genuine facial expressions of pain may qualify at best as semiautomatic systems, which require further validation by human observers before they can be used to validly assess facial expressions of pain.
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