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

From the 5th to the 8th October 2025, we visited the 2025 IEEE Conference on Systems, Men and Cypernetics, to present our research work. Our paper “ Multi-Modal AI-Based Pain Detection in Intermediate Care Patients in the Postoperative Phase” was accepted and we presented our progress in a poster session.

Abstract

“Multi-modal AI-Based Pain Detection in Inter- mediate 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.

Citing

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