title: Autonomous vision-guided approach for the analysis and grading of vertical suspension tests during Hammersmith Infant Neurological Examination (HINE).
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%.
authors.forename: Prasenjit
authors.initials: P
authors.lastname: Dey
authors.forename: Debi Prosad
authors.initials: DP
authors.lastname: Dogra
authors.forename: Partha Pratim
authors.initials: PP
authors.lastname: Roy
authors.forename: Harish
authors.initials: H
authors.lastname: Bhaskar
mesh_terms: D000465:Algorithms; D002675:Child, Preschool; D003116:Color; D006801:Humans; D007223:Infant; D007866:Leg / Q000502:physiology*; D008390:Markov Chains; D009460:Neurologic Examination / Q000379:methods*; D012880:Skin Pigmentation