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Related Concept Videos

Analgesia and Pain Management01:25

Analgesia and Pain Management

628
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...
628
Pain01:20

Pain

481
Pain serves as a critical warning signal that alerts the body to potential or actual harm. When mechanical pressure on the skin is intense, such as from a sharp pinch, the sensation transitions from touch to pain. Similarly, extreme temperatures, like a hot pot handle, convert the sensation of heat into pain. Pain can also result from overstimulation of other senses, such as blinding light, loud noise, or the intense heat from habañero peppers. This ability to sense pain is essential for...
481

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A Comprehensive Study on Pain Assessment from Multimodal Sensor Data.

Manuel Benavent-Lledo1, David Mulero-Pérez1, David Ortiz-Perez1

  • 1Department of Computer Technology, University of Alicante, 03080 Alicante, Spain.

Sensors (Basel, Switzerland)
|December 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces advanced computer vision for objective pain assessment, analyzing facial expressions to improve accuracy, especially for non-communicative patients. High accuracy was achieved, paving the way for better pain management.

Keywords:
computer visiondeep learningpain assessmentpattern recognitionsensor datasignal processing

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Pain Medicine

Background:

  • Traditional pain assessment relies on subjective patient reports, which can be inaccurate, particularly for patients with cognitive impairments.
  • Objective pain evaluation is crucial for timely interventions and effective patient care.
  • Existing methods lack robust, objective measures for individuals unable to communicate their pain effectively.

Purpose of the Study:

  • To develop and validate advanced computer vision techniques for objective pain assessment.
  • To analyze correlations between biomedical sensor data and facial expression analysis for pain detection.
  • To establish a baseline for pain assessment using state-of-the-art computer vision on established datasets.

Main Methods:

  • Utilized state-of-the-art computer vision, including Transformer-based architectures, to analyze facial expressions from video data.
  • Performed both per-frame and temporal context analysis of patient facial expressions.
  • Correlated biomedical sensor data with visual pain indicators.
  • Validated methods on the UNBC-McMaster Shoulder Pain Expression Archive Database and BioVid Heat Pain Database.

Main Results:

  • Achieved over 96% accuracy in pain estimation using single frames on the UNBC-McMaster dataset.
  • Attained over 94% for F1 Score, recall, and precision metrics in pain estimation.
  • Demonstrated the efficacy of computer vision techniques in objective pain assessment.
  • Provided a comparative baseline for pain assessment methods.

Conclusions:

  • Computer vision, particularly Transformer-based models, offers a highly accurate and objective approach to pain assessment.
  • Facial expression analysis significantly enhances pain detection capabilities, especially for non-verbal patients.
  • The study provides a strong foundation for future research in automated, objective pain management systems.