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Cross-domain self-supervised complete geometric representation learning for real-scanned point cloud based pathological gait analysis

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Title: Cross-domain self-supervised complete geometric representation learning for real-scanned point cloud based pathological gait analysis
Authors: Gu, X
Guo, Y
Yang, G-Z
Lo, B
Item Type: Journal Article
Abstract: Accurate lower-limb pose estimation is a prereq-uisite of skeleton based pathological gait analysis. To achievethis goal in free-living environments for long-term monitoring,single depth sensor has been proposed in research. However,the depth map acquired from a single viewpoint encodes onlypartial geometric information of the lower limbs and exhibitslarge variations across different viewpoints. Existing off-the-shelfthree-dimensional (3D) pose tracking algorithms and publicdatasets for depth based human pose estimation are mainlytargeted at activity recognition applications. They are relativelyinsensitive to skeleton estimation accuracy, especially at thefoot segments. Furthermore, acquiring ground truth skeletondata for detailed biomechanics analysis also requires consid-erable efforts. To address these issues, we propose a novelcross-domain self-supervised complete geometric representationlearning framework, with knowledge transfer from the unlabelledsynthetic point clouds of full lower-limb surfaces. The proposedmethod can significantly reduce the number of ground truthskeletons (with only 1%) in the training phase, meanwhileensuring accurate and precise pose estimation and capturingdiscriminative features across different pathological gait patternscompared to other methods.
Issue Date: 1-Mar-2022
Date of Acceptance: 22-Aug-2021
URI: http://hdl.handle.net/10044/1/91304
DOI: 10.1109/JBHI.2021.3107532
ISSN: 2168-2194
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 1034
End Page: 1044
Journal / Book Title: IEEE Journal of Biomedical and Health Informatics
Volume: 26
Issue: 3
Copyright Statement: © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information
Sponsor/Funder: British Council (UK)
British Council (UK)
Funder's Grant Number: 330760239
Publication Status: Published
Online Publication Date: 2021-08-27
Appears in Collections:Department of Surgery and Cancer
Faculty of Medicine
Institute of Global Health Innovation