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Vision-based gait impairment analysis for aided diagnosis

  • Javier Ortells
  • María Trinidad Herrero-Ezquerro
  • Ramón A. Mollineda
Original Article
  • 162 Downloads

Abstract

Gait is a firsthand reflection of health condition. This belief has inspired recent research efforts to automate the analysis of pathological gait, in order to assist physicians in decision-making. However, most of these efforts rely on gait descriptions which are difficult to understand by humans, or on sensing technologies hardly available in ambulatory services. This paper proposes a number of semantic and normalized gait features computed from a single video acquired by a low-cost sensor. Far from being conventional spatio-temporal descriptors, features are aimed at quantifying gait impairment, such as gait asymmetry from several perspectives or falling risk. They were designed to be invariant to frame rate and image size, allowing cross-platform comparisons. Experiments were formulated in terms of two databases. A well-known general-purpose gait dataset is used to establish normal references for features, while a new database, introduced in this work, provides samples under eight different walking styles: one normal and seven impaired patterns. A number of statistical studies were carried out to prove the sensitivity of features at measuring the expected pathologies, providing enough evidence about their accuracy.

Graphical Abstract

Graphical abstract reflecting main contributions of the manuscript: at the top, a robust, semantic and easy-to-interpret feature set to describe impaired gait patterns; at the bottom, a new dataset consisting of video-recordings of a number of volunteers simulating different patterns of pathological gait, where features were statistically assessed.

Keywords

Gait impairment Video-based gait analysis Gait database Computer-aided diagnosis 

Notes

Acknowledgements

The authors would like to thank the staff at Communication Sciences Laboratory (LABCOM) of Univ. Jaume I for their help in using these facilities.

Funding information

This work has been supported by grants P1-1B2015-74 and PREDOC/2012/05 from Univ. Jaume I, and TIN2013-46522-P from Spanish Ministry of Economy and Competitiveness.

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Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  1. 1.Institute of New Imaging TechnologiesUniversitat Jaume ICastellón de la PlanaSpain
  2. 2.School of Medicine, Department of Human Anatomy & PsychobiologyUniversidad de MurciaMurciaSpain

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