Pain assessment is essential in developing optimal pain management protocols to alleviate suffering and prevent functional decline in patients. Consequently, reliable and accurate automatic pain assessment systems are essential for continuous and effective patient monitoring. This study presents synthetic thermal videos generated by Generative Adversarial Networks integrated into the pain recognition pipeline and evaluates their efficacy. A framework consisting of a Vision-MLP and a Transformer-based module is utilized, employing RGB and synthetic thermal videos in unimodal and multimodal settings. Experiments conducted on facial videos from the BioVid database demonstrate the effectiveness of synthetic thermal videos and underline the potential advantages of it.

翻译：疼痛评估对于制定最优疼痛管理方案以减轻患者痛苦、防止功能衰退至关重要。因此，可靠且准确的自动疼痛评估系统对于持续有效的患者监护具有重要意义。本研究提出将生成对抗网络合成的热成像视频整合至疼痛识别流程，并评估其有效性。研究采用包含Vision-MLP模块与基于Transformer模块的框架，在单模态与多模态设置下分别运用RGB视频与合成热成像视频进行实验。在BioVid数据库面部视频上开展的实验验证了合成热成像视频的有效性，并揭示了其潜在优势。