Simulating dynamic facial expressions of pain from visuo-haptic interactions with a robotic patient
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Published version
Author(s)
Type
Journal Article
Abstract
Medical training simulators can provide a safe and controlled environment for medical students to practice their physical examination skills. An important source of information for physicians is the visual feedback of involuntary pain facial expressions in response to physical palpation on an affected area of a patient. However, most existing robotic medical training simulators that can capture physical examination behaviours in real-time cannot display facial expressions and comprise a limited range of patient identities in terms of ethnicity and gender. Together, these limitations restrict the utility of medical training simulators because they do not provide medical students with a representative sample of pain facial expressions and face identities, which could result in biased practices. Further, these limitations restrict the utility of such medical simulators to detect and correct early signs of bias in medical training. Here, for the first time, we present a robotic system that can simulate facial expressions of pain in response to palpations, displayed on a range of patient face identities. We use the unique approach of modelling dynamic pain facial expressions using a data-driven perception-based psychophysical method combined with the visuo-haptic inputs of users performing palpations on a robot medical simulator. Specifically, participants performed palpation actions on the abdomen phantom of a simulated patient, which triggered the real-time display of six pain-related facial Action Units (AUs) on a robotic face (MorphFace), each controlled by two pseudo randomly generated transient parameters: rate of change β and activation delay τ. Participants then rated the appropriateness of the facial expression displayed in response to their palpations on a 4-point scale from “strongly disagree” to “strongly agree”. Each participant (n=16, 4 Asian females, 4 Asian males, 4 White females and 4 White males) performed 200 palpation trials on 4 patient identities (Black female, Black male, White female and White male) simulated using MorphFace. Results showed facial expressions rated as most appropriate by all participants comprise a higher rate of change and shorter delay from upper face AUs (around the eyes) to those in the lower face (around the mouth). In contrast, we found that transient parameter values of most appropriate-rated pain facial expressions, palpation forces, and delays between palpation actions varied across participant-simulated patient pairs according to gender and ethnicity. These findings suggest that gender and ethnicity biases affect palpation strategies and the perception of pain facial expressions displayed on MorphFace. We anticipate that our approach will be used to generate physical examination models with diverse patient demographics to reduce erroneous judgments in medical students, and provide focused training to address these errors.
Date Issued
2022-03-10
Online Publication Date
2022-03-22T12:52:33Z
Date Acceptance
2022-02-28
ISSN
2045-2322
Publisher
Nature Publishing Group
Journal / Book Title
Scientific Reports
Volume
12
Copyright Statement
© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URI
Sponsor
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/R511547/1
EP/N03211X/2
EP/T00603X/1
Publication Status
Published
Article Number
ARTN 4200