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A study of gait acceleration and synchronisation in healthy adult subjects

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  • Mitsuru Yoneyama

Abstract

Accelerometry-based gait analysis is widely recognised as a promising tool in healthcare and clinical settings since it is unobtrusive, inexpensive and capable of providing insightful information on human gait characteristics. In order to expand the application of this technology in daily environments, it is desirable to develop reliable gait measures and their extraction methods from the acceleration signal that can differentiate between normal and atypical gait. Important examples of such measures are gait cycle and gait-induced acceleration magnitude, which are known to be closely related to each other depending on each individual's physical condition. In this study, we derive a model equation with two parameters which captures the essential relationships between gait cycle and gait acceleration based on experiments and physical modelling. We also introduce as a new gait parameter a set of indexes to evaluate the synchronisation behaviour of gait timing. The function and utility of the proposed parameters are examined in 11 healthy subjects during walking under various selected conditions.

Suggested Citation

  • Mitsuru Yoneyama, 2014. "A study of gait acceleration and synchronisation in healthy adult subjects," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 17(14), pages 1542-1552, October.
    • Handle: RePEc:taf:gcmbxx:v:17:y:2014:i:14:p:1542-1552
    DOI: 10.1080/10255842.2012.753069
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    References listed on IDEAS

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    1. Gates, Deanna H. & Su, Jimmy L. & Dingwell, Jonathan B., 2007. "Possible biomechanical origins of the long-range correlations in stride intervals of walking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 259-270.
    2. Bartsch, Ronny & Plotnik, Meir & Kantelhardt, Jan W. & Havlin, Shlomo & Giladi, Nir & Hausdorff, Jeffrey M., 2007. "Fluctuation and synchronization of gait intervals and gait force profiles distinguish stages of Parkinson's disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(2), pages 455-465.
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