Applying adversarial auto-encoder for estimating human walking gait abnormality index

  • Trong-Nguyen NguyenEmail author
  • Jean Meunier
Industrial and commercial application


This paper proposes an approach that estimates a human walking gait abnormality index using an adversarial auto-encoder (AAE), i.e., a combination of auto-encoder and generative adversarial network (GAN). Since most GAN-based models have been employed as data generators, our work introduces another perspective of their application. This method directly works on a sequence of 3D point clouds representing the walking postures of a subject. By fitting a cylinder onto each point cloud and feeding cylindrical histograms to an appropriate AAE, our system is able to provide different measures that may be used as gait abnormality indices. The combinations of such quantities are also investigated to obtain improved indicators. The ability of our method is demonstrated by experimenting on a large dataset of nearly 100 thousands point clouds, and the results outperform related approaches that employ different input data types.


Gait Adversarial Auto-encoder Point cloud Posture Depth camera Kinect Mirror 



The authors would like to thank the NSERC (Natural Sciences and Engineering Research Council of Canada) for supporting this work (Discovery Grant RGPIN-2015-05671). We also thank Hoang Anh Nguyen (Airspace Systems Inc., CA, USA) for useful discussions.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Image Processing Laboratory, DIROUniversity of MontrealMontrealCanada

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