NDLI: Using Facially Expressive Robots to Calibrate Clinical Pain Perception

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Using Facially Expressive Robots to Calibrate Clinical Pain Perception

Content Provider	ACM Digital Library
Author	Das, Sumit K. Popa, Dan O. Riek, Laurel D. Moosaei, Maryam
Abstract	In this paper, we introduce a novel application of social robotics in healthcare: high fidelity, facially expressive, robotic patient simulators (RPSs), and explore their usage within a clinical experimental context. Current commercially-available RPSs, the most commonly used humanoid robots worldwide, are substantially limited in their usability and fidelity due to the fact that they lack one of the most important clinical interaction and diagnostic tools: an expressive face. Using autonomous facial synthesis techniques, we synthesized pain both on a humanoid robot and comparable virtual avatar. We conducted an experiment with 51 clinicians and 51 laypersons (n = 102), to explore differences in pain perception across the two groups, and also to explore the effects of embodiment (robot or avatar) on pain perception. Our results suggest that clinicians have lower overall accuracy in detecting synthesized pain in comparison to lay participants. We also found that all participants are overall less accurate detecting pain from a humanoid robot in comparison to a comparable virtual avatar, lending support to other recent findings in the HRI community. This research ultimately reveals new insights into the use of RPSs as a training tool for calibrating clinicians' pain detection skills.
Starting Page	32
Ending Page	41
Page Count	10
File Format	PDF
ISBN	9781450343367
DOI	10.1145/2909824.3020216
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-03-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Facial expressions Social robots Humanoid robots Emotion Human robot interaction Patient simulation Non-verbal communication Affective communication Health informatics Medical technologies
Content Type	Text
Resource Type	Article

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