Automatic pain classification in older patients with hip fracture based on multimodal information fusion.

Given the limitations of unimodal pain recognition approaches, this study aimed to develop a multimodal pain recognition system for older patients with hip fractures using multimodal information fusion. The proposed system employs ResNet-50 for facial expression analysis and a VGG-based (VGGish) network for audio-based pain recognition. A channel attention mechanism was incorporated to refine feature representations and enhance the model's ability to distinguish between different pain levels. The outputs of the two unimodal systems were then integrated using a weighted-sum fusion strategy to create a unified multimodal pain recognition model. A self-constructed multimodal pain dataset was used for model training and validation, with the data split in an 80:20 ratio. Final testing was conducted using the BioVid Heat Pain Database. The VGGish model, optimized by a LSTM network and the channel attention mechanism, was trained on a hip fracture pain dataset, and the accuracy of the model was maintained at 80% after 500 iterations. The model was subsequently tested on the BioVid heat pain database, Pain grades 2 to 4. The confusion matrix test indicated an accuracy of 85% for Pain grade 4. This study presents the first clinically validated multimodal pain recognition system that integrates facial expression and speech data. The results demonstrate the feasibility and effectiveness of the proposed approach in real-world clinical environments.