Prediction of pain intensity using multimedia data

Prediction of pain intensity using multimedia data Increased medical expenditure, improper treatment, decreased productivity and above all inadequate pain, assessment is the global challenging problem, causing a substantial burden on the individual, their family, and society which often leads to loss of interest in life. The facial expression variation generally reflects clue for the occurrence of pain. This offers a vital aspect for non-verbal patients who are not in a position to rate their pain intensity level. The hypothesis proposes the developing of a multimodal machine based tool which will assess pain intensity. A framework has been designed to meet up with these specific issues which involve extracting features from the face and genes involved in pain. Patients suffering from pain require a comprehensive assessment to be conducted for proper diagnosis. The contributions of this paper are fourfold: Firstly, this paper provides an efficient approach to computational pain quantification. Secondly, it investigates the medical practitioner perception to pain along with the readings of the various tools available. Thirdly, the psychological aspect is taken into consideration to predict how pain is perceived by observers and experts (physicians). Fourthly, genes involved in pain and no pain conditions are taken and classified. Three large databases of spontaneous pain expressions are used i.e., McMaster UNBC Pain Archive database, self-prepared database and the other BioVid heat pain database of pain to verify the accuracy and robustness of the system. The methodology achieves 87% accuracy rate for classification of frames amid four levels of pain intensity. Designing intelligent computing systems with the given methodology will certainly improve the quality of life of patients. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Prediction of pain intensity using multimedia data

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4718-6
Publisher site
See Article on Publisher Site

Abstract

Increased medical expenditure, improper treatment, decreased productivity and above all inadequate pain, assessment is the global challenging problem, causing a substantial burden on the individual, their family, and society which often leads to loss of interest in life. The facial expression variation generally reflects clue for the occurrence of pain. This offers a vital aspect for non-verbal patients who are not in a position to rate their pain intensity level. The hypothesis proposes the developing of a multimodal machine based tool which will assess pain intensity. A framework has been designed to meet up with these specific issues which involve extracting features from the face and genes involved in pain. Patients suffering from pain require a comprehensive assessment to be conducted for proper diagnosis. The contributions of this paper are fourfold: Firstly, this paper provides an efficient approach to computational pain quantification. Secondly, it investigates the medical practitioner perception to pain along with the readings of the various tools available. Thirdly, the psychological aspect is taken into consideration to predict how pain is perceived by observers and experts (physicians). Fourthly, genes involved in pain and no pain conditions are taken and classified. Three large databases of spontaneous pain expressions are used i.e., McMaster UNBC Pain Archive database, self-prepared database and the other BioVid heat pain database of pain to verify the accuracy and robustness of the system. The methodology achieves 87% accuracy rate for classification of frames amid four levels of pain intensity. Designing intelligent computing systems with the given methodology will certainly improve the quality of life of patients.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jul 7, 2017

References

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