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Related Experiment Videos

Survey on Pain Detection Using Machine Learning Models: Narrative Review.

Ruijie Fang1, Elahe Hosseini1, Ruoyu Zhang1

  • 1Department of Electrical and Computer Engineering, University of California, Davis, CA, United States.

JMIR AI
|February 24, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Analgesia and Pain Management01:25

Analgesia and Pain Management

422
Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...
422

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Automated pain recognition shows promise using facial expressions and physiological signals. However, challenges in accuracy across diverse populations and contexts require further research for reliable clinical application.

Area of Science:

  • Biomedical Engineering
  • Pain Medicine
  • Human-Computer Interaction

Background:

  • Pain is a significant societal and clinical concern, driving the need for objective assessment methods.
  • Automated pain assessment technologies have advanced, offering potential solutions for clinical and daily use.

Purpose of the Study:

  • To survey automated pain recognition modalities and their underlying mechanisms.
  • To identify current challenges and future research avenues in automated pain assessment.

Main Methods:

  • A comprehensive literature review was performed.
  • Studies on facial expressions, physiological signals, audio cues, and pupil dilation were analyzed for pain recognition efficacy.

Main Results:

Keywords:
machine learningmobile phonepainpain assessmentsurvey

Related Experiment Videos

  • Facial expressions and physiological signals demonstrate significant potential for automated pain recognition.
  • Modality reliability and accuracy are influenced by individual variability and environmental factors.

    • Conclusions:

    • Automated pain recognition has advanced but faces challenges in consistent accuracy across diverse settings.
    • Future research should focus on improving reliability and applicability for clinical integration.