Title
Pain monitoring: A dynamic and context-sensitive system
Abstract
The current paper presents an automatic and context sensitive system for the dynamic recognition of pain expression among the six basic facial expressions and neutral on acted and spontaneous sequences. A machine learning approach based on the Transferable Belief Model, successfully used previously to categorize the six basic facial expressions in static images [2,61], is extended in the current paper for the automatic and dynamic recognition of pain expression from video sequences in a hospital context application. The originality of the proposed method is the use of the dynamic information for the recognition of pain expression and the combination of different sensors, permanent facial features behavior, transient features behavior, and the context of the study, using the same fusion model. Experimental results, on 2-alternative forced choices and, for the first time, on 8-alternative forced choices (i.e. pain expression is classified among seven other facial expressions), show good classification rates even in the case of spontaneous pain sequences. The mean classification rates on acted and spontaneous data reach 81.2% and 84.5% for the 2-alternative and 8-alternative forced choices, respectively. Moreover, the system performances compare favorably to the human observer rates (76%), and lead to the same doubt states in the case of blend expressions.
Year
DOI
Venue
2012
10.1016/j.patcog.2011.09.014
Pattern Recognition
Keywords
Field
DocType
2-alternative forced choice,current paper,spontaneous pain sequence,permanent facial features behavior,facial expression,basic facial expression,context-sensitive system,dynamic recognition,pain expression,blend expression,8-alternative forced choice,pain monitoring,classification
Categorization,Expression (mathematics),Pattern recognition,Spontaneous pain,Originality,Facial expression,Artificial intelligence,Transferable belief model,Observer (quantum physics),Mathematics,Machine learning
Journal
Volume
Issue
ISSN
45
4
0031-3203
Citations 
PageRank 
References 
26
0.99
30
Authors
2
Name
Order
Citations
PageRank
Zakia Hammal115613.67
Miriam Kunz2352.89