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Efficient Pain Recognition via Respiration Signals: A Single Cross-Attention Transformer Multi-Window Fusion Pipeline

Created by
  • Haebom

Author

Stefanos Gkikas, Ioannis Kyprakis, Manolis Tsiknakis

Outline

This paper proposes an automatic pain assessment system using respiration. This research, submitted to the AI4PAIN Challenge, introduces a pipeline that combines a highly efficient cross-attention transformer with a multi-windowing strategy. Experimental results demonstrate that respiration is a useful physiological indicator for pain assessment, and that an optimized small model can outperform a large model. The multi-windowing approach effectively captures short-term and long-term features as well as overall characteristics, enhancing the model's representational power.

Takeaways, Limitations

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Takeaways:
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Confirming that breathing is a useful physiological indicator for pain assessment.
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It is suggested that an optimized small model can outperform a large model.
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We demonstrate that the multi-window approach is effective in improving the model's expressive ability.
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Presenting the potential for continuous pain monitoring and clinical decision support.
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Limitations:
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This study was submitted to the AI4PAIN Challenge and requires validation in an actual clinical environment.
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Further research is needed to explore the potential for performance enhancement through combination with other physiological indicators beyond respiration.
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Further research is needed to determine generalizability across different pain types and intensities.
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Further research is needed to determine the model's interpretability and reliability.
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