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Computer mediated automatic detection of pain-related behavior: prospect, progress, perils
Kenneth M Prkachin1, Zakia Hammal2
1Department of Psychology, University of Northern British Columbia, Prince George, BC, Canada.
Frontiers in Pain Research (Lausanne, Switzerland)
|February 17, 2022
Summary
Automated pain assessment using computer vision and machine learning shows promise but requires rigorous validation. Careful development and data collection are crucial to avoid bias and ensure reliable pain measurement.
Area of Science:
- Pain research
- Affective computing
- Behavioral science
Background:
- Pain is subjective, but assessment relies on observable behaviors, particularly facial expressions.
- Traditional behavioral pain assessment is labor-intensive and impractical in many settings.
- Affective computing offers technological solutions for automated pain assessment.
Purpose of the Study:
- To explore the potential of computer vision and machine learning (CVML) for automated pain assessment.
- To review the progress and challenges in developing CVML-based pain measurement systems.
- To highlight the need for rigorous validation and ethical considerations in automated pain assessment.
Main Methods:
- Review of existing literature on behavioral pain indices and CVML applications.
- Analysis of studies developing and testing automated facial pain expression recognition.
- Exploration of multimodal approaches and commercial product development.
Main Results:
- CVML can track facial expressions of pain, with numerous studies demonstrating feasibility.
- Research in automated pain assessment has rapidly expanded.
- Commercial products for real-time pain measurement are emerging.
Conclusions:
- Automated pain assessment technology is still nascent, with risks of overpromising.
- Adherence to measurement principles and population-specific validation is critical.
- Development of standardized, privacy-compliant databases and awareness of technological limitations are needed.
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