Emotion Recognition in Robotic Healthcare: A New Approach to Mitigating Professional Burnout Syndrome
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Paper in proceedings |
| Conference | IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
| MU Faculty or unit | |
| Citation | |
| Doi | https://doi.org/10.1109/SMC58881.2025.11342585 |
| Keywords | Robot; Healthcare; Emotion detection; Professional Burnout Syndrome |
| Description | Professional Burnout Syndrome (PBS) among health-care professionals has been considered a threat to both staff well-being and patient safety, especially in a high-stress medical environment. While robotics and AI have been increasingly integrated into healthcare, their impact on PBS has not been explored in detail yet. Therefore, this paper proposes a real-time PBS detection framework for healthcare professionals using emotion recognition. This framework includes a deep learning-based emotion detection system for humanoid companion robots, which then correlates emotional trends with the Circumplex model to identify burnout risk. Our experimental evaluation results across five deep learning architectures, MobileNet, RegNetY, Swin Transformer, ConvNeXt V2, and EVA-02, show a highest accuracy of 74.05% on a public emotion dataset. These results demonstrate the feasibility of integrating such systems into healthcare workflows for early PBS warnings. Also, this work suggests a human-in-the-loop diagnostic model, where robotic emotion detection complements clinical expertise by providing proactive support, strengthening workforce resilience, and maintaining the quality of patient care. |
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