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Manual Facial Action Coding studies (FACS) have discovered a fuzzy facial expression that is both specific and sensitive to pain. However, manual pain coding imposes limitations such as training time and effort, technological requirements and human subjective factors. To surmount these challenges, in the last decade and a half, devices embedded with artificial neural networks (ANNs) have been used in researching pain through facial expression. Using neuralnetwork theory, this paper argues that face perception of pain is organized around 'fuzzy' cases such that human observers judge a pain face based on their recognition that one face is more or less similar to other faces whose results are remembered and assessed ('fuzzy case based reasoning'). A study implementing a fuzzy case-based reasoning system integrated with an ANN (FCBR-ANN) produced more than 90% accuracy in pain perception. Face perception of pain using an FCBR-ANN may be a real-time alternative to manual coding of pain by human observers, and may prove clinically useful.

Item Type:	Refereed Conference Paper
Keywords:	artificial neural network, CBR, fuzzy case-based reasoning, pain detection
Research Division:	Philosophy and Religious Studies
Research Group:	Philosophy
Research Field:	Philosophy not elsewhere classified
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in human society
UTAS Author:	van Rysewyk, SP (Mr Simon Van Rysewyk)
ID Code:	118667
Year Published:	2016
Deposited By:	School of Humanities
Deposited On:	2017-07-18
Last Modified:	2017-08-24
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