Dissertation/Thesis Abstract

Computational pain quantification and the effects of age, gender, culture and cause
by Ostberg, Colin R., M.S., Marquette University, 2014, 62; 1554606
Abstract (Summary)

Chronic pain affects more than 100 million Americans and more than 1.5 billion people worldwide. Pain is a multidimensional construct, expressed through a variety of means. Facial expressions are one such type of pain expression. Automatic facial expression recognition, and in particular pain expression recognition, are fields that have been studied extensively. However, nothing has explored the possibility of an automatic pain quantification algorithm, able to output pain levels based upon a facial image.

Developed for a remote monitoring context, a computational pain quantification algorithm has been developed and validated by two distinct sets of data. The second set of data also included associated data for the fields of age, gender, culture and cause of pain. These four fields were investigated for their effect on automatic pain quantification, determining that age and gender have a definite impact and should be involved in the algorithm, while culture and cause require further investigation.

Indexing (document details)
Advisor: Ahamed, Sheikh I.
Commitee: Kaczmarek, Thomas, Maadooliat, Medhi
School: Marquette University
Department: Mathematics, Statistics and Computer Science
School Location: United States -- Wisconsin
Source: MAI 52/06M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Mathematics, Statistics, Computer science
Keywords: Automatic pain assessment, Eigenface, Mobile health, Remote monitoring
Publication Number: 1554606
ISBN: 978-1-303-85609-9
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