Twins-PainViT: Towards a Modality-Agnostic Vision Transformer Framework for Multimodal Automatic Pain Assessment using Facial Videos and fNIRS

Automatic pain assessment plays a critical role for advancing healthcare and optimizing pain management strategies. This study has been submitted to the First Multimodal Sensing Grand Challenge for Next-Gen Pain Assessment (AI4PAIN). The proposed multimodal framework utilizes facial videos and fNIRS and presents a modality-agnostic approach, alleviating the need for domain-specific models. Employing a dual ViT configuration and adopting waveform representations for the fNIRS, as well as for the extracted embeddings from the two modalities, demonstrate the efficacy of the proposed method, achieving an accuracy of 46.76% in the multilevel pain assessment task.

Synthetic Thermal and RGB Videos for Automatic Pain Assessment utilizing a Vision-MLP Architecture

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.

Multi-task Neural Networks for Pain Intensity Estimation using Electrocardiogram and Demographic Factors

Pain is a complex phenomenon which is manifested and expressed by patients in various forms. The immediate and objective recognition of it is a great of importance in order to attain a reliable and unbiased healthcare system. In this work, we elaborate electrocardiography signals revealing the existence of variations in pain perception among different demographic groups. We exploit this insight by introducing a novel multi-task neural network for automatic pain estimation utilizing the age and the gender information of each individual, and show its advantages compared to other approaches.