3D Limb Movement Tracking and Analysis for Neurological Dysfunctions of Neonates Using Multi-Camera Videos
Paper in proceeding, 2016

Central nervous system dysfunction in infants may be manifested through inconsistent, rigid and abnormal limb movements. Detection of limb movement anomalies associated with such neurological dysfunctions in infants is the first step towards early treatment for improving infant development. This paper addresses the issue of detecting and quantifying limb movement anomalies in infants through non-invasive 3D image analysis methods using videos from multiple camera views. We propose a novel scheme for tracking 3D time trajectories of markers on infant’s limbs by video analysis techniques. The proposed scheme employ videos captured from three camera views. This enables us to detect a set of enhanced 3D markers through cross-view matching and to effectively handle marker self-occlusions by other body parts. We track a set of 3D trajectories of limb movements by a set of particle filters in parallel, enabling more robust 3D tracking of markers, and use the 3D model errors for quantifying abrupt limb movements. The proposed work makes a significant advancement to the previous work in [1] through employing tracking in 3D space, and hence overcome several main barriers that hinder real applications by using single camera-based techniques. To the best of our knowledge, applying such a multi-view video analysis approach for assessing neurological dysfunctions of infants through 3D time trajectories of markers on limbs is novel, and could lead to computer-aided tools for diagnosis of dysfunctions where early treatment may improve infant development. Experiments were conducted on multi-view neonate videos recorded in a clinical setting and results have provided further support to the proposed method.

Neonates

3D trajectory tracking

multi-camera video

neurological dysfunction quantification

Author

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Grzegorz Sowulewski

Chalmers, Signals and Systems

Yixiao Yun

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Anders Flisberg

University of Gothenburg

Magnus Thordstein

University of Gothenburg

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

1557170X (ISSN)

Vol. 2016-October 2395-2398 7591212
978-1-4577-0220-4 (ISBN)

Areas of Advance

Information and Communication Technology

Life Science Engineering (2010-2018)

Subject Categories

Signal Processing

Neurology

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/EMBC.2016.7591212

ISBN

978-1-4577-0220-4

More information

Latest update

4/5/2022 6