# Multimodal Model for Automated Pain Assessment: Leveraging Video and fNIRS
## 6 Conclusions  
Our proposed method demonstrated a high validation accuracy. Its validation accuracy reached up to 62.7% for both the video + fNIRS and the video + manual statistical features configurations. For the final submission, we chose to report results using the video + fNIRS modality, as it showed the best performance in internal validation. However, our model achieved only 51.33% accuracy on the hidden test set. This performance gap can be primarily attributed to the high class imbalance in the test dataset, where the \"No Pain\" category was significantly underrepresented. A simple up-sampling strategy to generate more \"No Pain\" samples proved ineffective for our model. Moreover, since the challenge was designed to reward the highest overall accuracy, we prioritized optimizing for general performance and did not conduct extensive experiments specifically aimed at improving the \"No Pain\" classification. The fNIRS modality faced challenges due to artifact noise, which is a known issue that requires rigorous preprocessing for optimal signal quality. Our artifact filtering approach partially mitigated these effects, although some residual noise may have impacted the final model performance. This noise is typically introduced by ambient lighting, subject movement, or improper sensor placement, resulting in signal contamination and information loss. As a result, physiological patterns became harder to distinguish, making it more difficult for the model to learn meaningful features from the fNIRS data. Without thorough preprocessing--such as the artifact filtering discussed in this study--fNIRS data remain highly susceptible to noise, limiting its standalone effectiveness in pain classification tasks. In contrast, our experiments on the BioVid Heat Pain database demonstrated that the GSR modality provided more discriminative features for pain detection than the video modality alone. The fusion of GSR and video using our attention-based model achieved the highest accuracy in the 10-fold cross-validation setting, outperforming both unimodal baselines and prior models such as PainAttNet. These results support the value of physiological signals in automatic pain recognition and highlight the effectiveness of our multimodal attention-based fusion approach in capturing complementary information. These findings underscore the complexity of pain classification and highlight the challenge of achieving balanced sensitivity across all pain categories. Future work should explore more advanced fusion strategies, semi-supervised or self-supervised learning to leverage underrepresented classes such as \"No Pain\" and the inclusion of additional datasets or modalities (e.g., EEG or EMG) to further enhance generalization. Testing the model in real-world clinical scenarios would also be a valuable next step to evaluate its practical applicability. Conceptualization, J.V., H.Y., S.Y., S.K. ([PERSON]), J.S. and S.K. ([PERSON]); methodology, J.V., H.Y., S.Y., S.K. ([PERSON]), [PERSON] and S.K. ([PERSON]); software, J.V. and A.D.; original draft preparation, J.V. ; draft review, editing and language error checking, A.D. and S.Y. ; supervision and project administration were performed by [PERSON] All authors have read and agreed to the published version of the manuscript.