Work

Continuous, Contactless, and Multimodal Pain Assessment During Surgical Interventions

Year: 2025

Type: article

Abstract: Goal: We introduce a continuous, multimodal pain classification technique that utilizes camera-based data conducted in clinical settings.Methods: We integrate facial Action Units (AUs) obtained from samples with sequential vital parameters extracted from video data, and systematically validate the practicality of measuring heart rate variability (HRV) from video-derived photoplethysmographic signals against traditional sensor-based electrocardiogram measurements. Video-based AUs and HRV metrics acquired from ultra-short-term processing are combined into an automated, contactless, multimodal algorithm for binary pain classification. Utilizing logistic regression alongside leave-one-out cross-validation, this approach is developed and validated using the BioVid Heat Pain Database and subsequently tested with our surgical Individual Patient Data.Results: We achieve an F1-score of 53% on the BioVid Heat Pain Database and 48% on our Individual Patient Data with ultra-short-term processing.Conclusion: Our approach provides a robust foundation for future multimodal pain classification utilizing vital signs and mimic parameters from 5.5 s camera recordings.

Source: IEEE Open Journal of Engineering in Medicine and Biology

DOI: 10.1109/ojemb.2025.3633051

Authors: Bianca Reichard, Mirco Fuchs, Kerstin Bode

Institutions: Leipzig University of Applied Sciences, Institut für Angewandte Informatik (InfAI) e. V., Leipzig Heart Institute, Leipzig University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,075