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Please use this identifier to cite or link to this item: http://idr.iitbbs.ac.in/jspui/handle/2008/933
Title: Autonomous vision-guided approach for the analysis and grading of vertical suspension tests during Hammersmith Infant Neurological Examination (HINE)
Authors: Dey P.
Dogra D.P.
Roy P.P.
Bhaskar H.
Keywords: Examination automation
HINE
Infant neurological examinations
Limb tracking
Pattern analysis
Issue Date: 2016
Citation: 1
Abstract: Computer vision assisted diagnostic systems are gaining popularity in different healthcare applications. This paper presents a video analysis and pattern recognition framework for the automatic grading of vertical suspension tests on infants during the Hammersmith Infant Neurological Examination (HINE). The proposed vision-guided pipeline applies a color-based skin region segmentation procedure followed by the localization of body parts before feature extraction and classification. After constrained localization of lower body parts, a stick-diagram representation is used for extracting novel features that correspond to the motion dynamic characteristics of the infant's leg movements during HINE. This set of pose features generated from such a representation includes knee angles and distances between knees and hills. Finally, a time-series representation of the feature vector is used to train a Hidden Markov Model (HMM) for classifying the grades of the HINE tests into three predefined categories. Experiments are carried out by testing the proposed framework on a large number of vertical suspension test videos recorded at a Neuro-development clinic. The automatic grading results obtained from the proposed method matches the scores of experts at an accuracy of 74%. � 2016 IEEE.
URI: http://dx.doi.org/10.1109/EMBC.2016.7590837
http://10.10.32.48:8080/jspui/handle/2008/933
Appears in Collections:Research Publications

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