Establishing Pose Based Features Using Histograms for the Detection of Abnormal Infant Movements

Kevin McCay, Edmond S. L. Ho, Claire Marcroft, Nicholas D. Embleton

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)
58 Downloads (Pure)

Abstract

The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In this paper, we conducted a pilot study on extracting important information from video sequences to classify the body movement into two categories, normal and abnormal, and compared the results provided by an independent expert reviewer based on GMA. We present two new pose-based features, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D), for the pose-based analysis and classification of infant body movement from video footage. We extract the 2D skeletal joint locations from 2D RGB images using Cao et al.’s method [1]. Using the MINI-RGBD dataset [2], we further segment the body into local regions to extract part specific features. As a result, the pose and the degree of displacement are represented by histograms of normalised data. To demonstrate the effectiveness of the proposed features, we trained several classifiers using combinations of HOJO2D and HOJD2D features and conducted a series of experiments to classify the body movement into categories. The classification algorithms used included k-Nearest Neighbour (kNN, k=1 and k=3), Linear Discriminant Analysis (LDA) and the Ensemble classifier. Encouraging results were attained, with high accuracy (91.67%) obtained using the Ensemble classifier.
Original languageEnglish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (EMBC 2019)
Subtitle of host publicationBerlin, Germany , July 23-27, 2019
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages5469-5472
ISBN (Electronic)9781538613115
ISBN (Print)9781538613122
DOIs
Publication statusPublished - 2019

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