Title | PainFusion: Multimodal Pain Assessment from RGB and Sensor Data |
---|
Authors | Benavent-Lledo,M., Lopez-Valle, M., Ortiz-Perez, D., Mulero-Perez, D., Garcia-Rodriguez, J. and Psarrou, A. |
---|
Editors | Quintian, H., Corchado, E., Troncoso Lora, A., Perez-Garcia, H., Jove, E,, Calvo Role, J., Martinez de Pison, F., Garcia Bringas, P., Martinez Alvarez, F., Herrero, A. and Fosci, P. |
---|
Type | Conference paper |
---|
Abstract | Traditional pain assessment tools often rely on subjective self-reporting methods, hindering the work of healthcare professionals. However, the patient’s facial expressions and biomedical data provide a reliable source of information for caregivers. In this work, we present a multimodal architecture that utilizes both RGB video and biomedical sensor data from the BioVid Heat Pain dataset. We use video transformer architectures in conjunction with a thorough analysis of biomedical signals, including galvanic skin response, electromyography, and electrocardiogram, for comprehensive feature extraction. These features are then fused to create a robust model for pain assessment. Experimental results show that our multimodal architecture outperforms unimodal video-based methods in pain detection. Furthermore, our study highlights the potential of combining non-invasive video analysis with physiological data to facilitate pain prediction and management in clinical settings, paving the way for more accurate and efficient pain assessment methods that can be used in various healthcare applications. |
---|
Keywords | pain assessment; computer vision; deep learning; sensor data; signal processing |
---|
Year | 2024 |
---|
Conference | 19th International Conference on Soft Computing Models in Industrial and Environmental Applications |
---|
Publisher | Springer Nature |
---|
Accepted author manuscript | File Access Level Open (open metadata and files) |
---|
Publication dates |
---|
Published | 09 Oct 2024 |
---|