Multi-Modal AI-Based Pain Detection in Intermediate Care Patients in the Postoperative Phase

Multi-Modal AI-Based Pain Detection in Intermediate Care Patients in the Postoperative Phase

“Multi-modal AI-Based Pain Detection in Intermediate Care Patients in the Postoperative Phase” is an interdisciplinary research work that operates in the domain of automated pain detection. It aims to improve previous work, based on pain databases like BioVid and UNBC shoulder pain, as well as AI-based approaches using computer vision and signal processing to analyze available modalities. Thus, we present our basic research idea on how to improve automatic pain detection in three major steps. The first step focuses on collecting pain data from postoperative patients in intermediate care stations (IMC). In addition, patients who are not fully oriented should be included in a separate data collection as a second focus group. Then, improvements on the state-of-the-art models should not only advance general pain detection, but also help bridge the gap to the real-world setting of the IMC data. Improvements include transferability analysis, feature selection evaluation, and balancing of data distribution to deliver better classification performance. In a last step, we aim to test, verify and evaluate the classification performance on the IMC data with the support of medical practitioners.

Fulltext Access

https://ieeexplore.ieee.org/document/11343734

Citing

@INPROCEEDINGS{Nienaber2025,

  author={Nienaber, Sören and Wang, Huibin and Hempel, Thorsten and Walter, Steffen and Barth, Eberhard and Al-Hamadi, Ayoub},

  booktitle={2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, 

  title={Multi-Modal AI-Based Pain Detection in Intermediate Care Patients in the Postoperative Phase}, 

  year={2025},

  doi={10.1109/SMC58881.2025.11343734}}