Abstract
Within the clinical education community, there is a desire to improve learners’ pain observation skills. Virtual patients can be used as a training tool for this purpose. In this paper, we present a pioneering approach for synthesizing naturalistic pain on virtual patients. Using the UNBC-McMaster pain archive and a CLM-based face tracker, we performed naturalistic pain synthesis. We conducted an experiment to validate our synthesis approach and compared it to manual methods that use FACS-trained animators. Our results suggest that our approach was effective, and yielded higher pain labeling accuracies compared to manually animated painful faces. This research offers a new tool to both the virtual patient and clinical education communities.
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Moosaei, M., Gonzales, M.J., Riek, L.D. (2014). Naturalistic Pain Synthesis for Virtual Patients. In: Bickmore, T., Marsella, S., Sidner, C. (eds) Intelligent Virtual Agents. IVA 2014. Lecture Notes in Computer Science(), vol 8637. Springer, Cham. https://doi.org/10.1007/978-3-319-09767-1_38
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DOI: https://doi.org/10.1007/978-3-319-09767-1_38
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