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A Personalized Spatial-Temporal Cold Pain Intensity Estimation Model Based on Facial Expression.

Yikang Guo1, Li Wang1, Yan Xiao2

  • 1Intelligent Human-Machine Systems LabMechanical and Industrial Engineering DepartmentCollege of Engineering, Northeastern University Boston MA 02115 USA.

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|October 15, 2021
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Summary
This summary is machine-generated.

Facial expression analysis accurately estimates cold pain intensity. A personalized, spatial-temporal model using Convolutional LSTM achieved the highest performance, offering objective pain assessment.

Keywords:
Cold painfacial expressionpersonalized modeltemporal information

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Area of Science:

  • Medical Informatics
  • Computer Vision
  • Pain Management

Background:

  • Pain assessment is crucial in clinical settings and research.
  • Facial expression analysis offers a convenient, automatic, real-time method for pain detection.

Purpose of the Study:

  • To conduct a cold pain intensity estimation experiment using facial expressions.
  • To evaluate the significance of spatial-temporal information in pain estimation.
  • To compare the performance of personalized and generalized models for pain assessment.

Main Methods:

  • Facial expressions from 29 subjects undergoing a cold pain experiment were analyzed.
  • Three architectures (Inception V3, VGG-LSTM, Convolutional LSTM) were employed to estimate pain intensities (No pain, Moderate, Severe).
  • Spatial-temporal models were compared against single-frame approaches, and personalized vs. generalized models were evaluated.

Main Results:

  • Convolutional LSTM with a personalized model achieved a mean F1 score of 79.48%.
  • Spatial-temporal information significantly improved pain estimation accuracy compared to single-frame methods.
  • Personalized models demonstrated superior performance over generalized models.

Conclusions:

  • Facial expression analysis holds significant potential for estimating cold pain intensity.
  • A personalized, spatial-temporal framework provides enhanced accuracy for objective pain assessment.