Skip navigation
Please use this identifier to cite or link to this item:
Title: Posture recognition in HINE exercises
Authors: Ansari A.F.
Pratim Roy, Partha
Dogra D.P.
Kumar S.
Raman B.
Roy [initials]P.P.
Sen D.
Published in: Proceedings of Advances in Intelligent Systems and Computing
Abstract: Pattern recognition, image and video processing based automatic or semi-automatic methodologies are widely used in healthcare services. Especially, image and video guided systems have successfully replaced various medical processes including physical examinations of the patients, analyzing physiological and bio-mechanical parameters, etc. Such systems are becoming popular because of their robustness and acceptability amongst the healthcare community. In this paper, we present an efficient way of infant’s posture recognition in a given video sequence of Hammersmith Infant Neurological Examinations (HINE). Our proposed methodology can be considered as a step forward in the process of automating HINE tests through computer assisted tools. We have tested our methodology with a large set of HINE videos recorded at the neuro-development clinic of hospital. It has been found that the proposed methodology can successfully classify the postures of infants with an accuracy of 78.26 %. © Springer Science+Business Media Singapore 2017.
Citation: Proceedings of Advances in Intelligent Systems and Computing, (2017), 321- 330
Issue Date: 2017
Publisher: Springer Verlag
Keywords: Hidden Markov model
HINE tests
Posture recognition
Skin segmentation
ISBN: 9.78981E+12
ISSN: 21945357
Author Scopus IDs: 57192963728
Author Affiliations: Ansari, A.F., Department of Civil Engineering, IIT Roorkee, Roorkee, 247667, India
Roy, P.P., Department of Computer Science & Engineering, IIT Roorkee, Roorkee, 247667, India
Dogra, D.P., School of Electrical Sciences, IIT Bhubaneswar, Bhubaneswar, 751013, India
Corresponding Author: Ansari, A.F.; Department of Civil Engineering, IIT RoorkeeIndia; email:
Appears in Collections:Conference Publications [CS]

Files in This Item:
There are no files associated with this item.
Show full item record

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.