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FER-PCVT: Facial Expression Recognition with Patch-Convolutional Vision Transformer for Stroke Patients.

Yiming Fan1, Hewei Wang2, Xiaoyu Zhu1

  • 1School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.

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|December 23, 2022
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Summary

This study introduces a lightweight facial expression recognition algorithm to automatically assess stroke patients' motivation during rehabilitation. The model enhances recovery insights by analyzing expressions, reducing medical resource strain.

Keywords:
convolutional neural networks (CNNs)facial expression recognition (FER)rehabilitationstrokevision transformer (ViT)

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Stroke rehabilitation requires precise intensity monitoring, currently reliant on subjective physician assessments.
  • Current methods for evaluating rehabilitation intensity strain medical resources.
  • Automating the assessment of patient motivation is crucial for optimizing physical recovery.

Purpose of the Study:

  • To develop a lightweight facial expression recognition algorithm for automatic diagnosis of stroke patients' training motivation.
  • To improve the efficiency and accuracy of rehabilitation intensity assessment.
  • To reduce the burden on medical professionals and resources.

Main Methods:

  • Integrating convolutional properties into Vision Transformer for local and global facial expression feature extraction.
  • Adopting a pyramid-shaped feature output from Convolutional Neural Networks to minimize parameters and computational cost.
  • Designing a specialized classifier for enhanced facial expression classification in stroke patients.

Main Results:

  • The proposed algorithm demonstrated superior performance compared to Pyramid Vision Transformer (PvT) and Convolutional Vision Transformer (CvT) in terms of parameters and FLOPs.
  • Achieved 89.44% accuracy on the Real-world Affective Faces Database (RAF-DB), surpassing recent studies.
  • Attained 99.81% accuracy on a private dataset of stroke patients with only 4.10M parameters.

Conclusions:

  • The developed lightweight facial expression recognition algorithm effectively diagnoses stroke patients' training motivation.
  • The algorithm offers a computationally efficient and highly accurate solution for rehabilitation monitoring.
  • This technology has the potential to significantly aid in optimizing stroke patient recovery and reducing healthcare costs.