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@article{Gu:2022:10.1109/JBHI.2021.3107532,
author = {Gu, X and Guo, Y and Yang, G-Z and Lo, B},
doi = {10.1109/JBHI.2021.3107532},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1034--1044},
title = {Cross-domain self-supervised complete geometric representation learning for real-scanned point cloud based pathological gait analysis},
url = {http://dx.doi.org/10.1109/JBHI.2021.3107532},
volume = {26},
year = {2022}
}

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TY  - JOUR
AB  - 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.
AU  - Gu,X
AU  - Guo,Y
AU  - Yang,G-Z
AU  - Lo,B
DO  - 10.1109/JBHI.2021.3107532
EP  - 1044
PY  - 2022///
SN  - 2168-2194
SP  - 1034
TI  - Cross-domain self-supervised complete geometric representation learning for real-scanned point cloud based pathological gait analysis
T2  - IEEE Journal of Biomedical and Health Informatics
UR  - http://dx.doi.org/10.1109/JBHI.2021.3107532
UR  - http://hdl.handle.net/10044/1/91304
VL  - 26
ER  -

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