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Acute Pain Recognition from Facial Expression Videos using Vision Transformers
Summary
Automated pain detection using video vision transformers (ViViT) shows promise for patients with communication disabilities. This AI approach accurately estimates pain levels from facial expressions, aiding clinical assessment.
Area of Science:
- Computer Science
- Biomedical Engineering
- Artificial Intelligence
Background:
- Accurate pain assessment is crucial for patient diagnosis and treatment.
- Automated pain detection from facial expressions aids patients with communication disabilities.
Purpose of the Study:
- To develop and evaluate video vision transformers (ViViT) for automated pain recognition.
- To capture spatio-temporal facial information for binary pain classification.
Main Methods:
- Trained and evaluated ViViT models on two acute pain datasets: AI4PAIN Challenge and BioVid Pain.
- Compared ViViT performance against baseline models: ResNet50 and ResNet50+3DCNN.
Main Results:
- ViViT achieved 66.96% accuracy on the AI4PAIN dataset.
- ViViT achieved 79.95% accuracy on the BioVid dataset, outperforming baseline models.
Conclusions:
- The proposed ViViT model demonstrates superior performance in automated pain detection from facial expressions.
- This technology offers valuable insights for objective pain estimation in clinical settings.
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