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Methods for Gait Analysis During Obstacle Avoidance Task

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Abstract

In this study, we present algorithms developed for gait analysis, but suitable for many other signal processing tasks. A novel general-purpose algorithm for extremum estimation of quasi-periodic noisy signals is proposed. This algorithm is both flexible and robust, and allows custom adjustments to detect a predetermined wave pattern while being immune to signal noise and variability. A method for signal segmentation was also developed for analyzing kinematic data recorded while performing on obstacle avoidance task. The segmentation allows detecting preparation and recovery phases related to obstacle avoidance. A simple kernel-based clustering method was used for classification of unsupervised data containing features of steps within the walking trial and discriminating abnormal from regular steps. Moreover, a novel algorithm for missing data approximation and adaptive signal filtering is also presented. This algorithm allows restoring faulty data with high accuracy based on the surrounding information. In addition, a predictive machine learning technique is proposed for supervised multiclass labeling with non-standard label structure.

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Acknowledgments

Special thanks to Mr. Gecht Gilad and Mr. Aharoni Nir for implementation and testing of the algorithm predicting input parameters for CCFA. This research was supported in part by the European Commission (FP7 Project V-TIME- 278169).

Conflict of interest

Virtual reality for movement disorder diagnosis and/or treatment; A patent application on the use of virtual reality has been submitted, the intellectual property rights for are held by the Tel Aviv Medical Center.

Author information

Authors and Affiliations

  1. Faculty of Engineering, Holon Institute of Technology, Holon, Israel

    Dmitry Patashov & Ronen Sosnik

  2. Faculty of Sciences, Holon Institute of Technology, 52 Golomb Street, Holon, 5883754, Israel

    Dmitry Patashov, Yakir Menahem, Ohad Ben-Haim & Dmitry Goldstein

  3. Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Eran Gazit, Inbal Maidan, Anat Mirelman & Jeffrey M. Hausdorff

  4. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

    Inbal Maidan, Anat Mirelman & Jeffrey M. Hausdorff

  5. Department of Neurology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

    Inbal Maidan & Anat Mirelman

  6. Department of Physical Therapy, Tel Aviv University, Tel Aviv, Israel

    Jeffrey M. Hausdorff

  7. Rush Alzheimer’s Disease Center and Department of Orthopaedic Surgery, Rush University, Chicago, IL, USA

    Jeffrey M. Hausdorff

Authors
  1. Dmitry Patashov
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  2. Yakir Menahem
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  4. Eran Gazit
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  5. Inbal Maidan
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  9. Jeffrey M. Hausdorff
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Correspondence to Dmitry Patashov.

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Associate Editor Joel D Stitzel oversaw the review of this article.

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Patashov, D., Menahem, Y., Ben-Haim, O. et al. Methods for Gait Analysis During Obstacle Avoidance Task. Ann Biomed Eng 48, 634–643 (2020). https://doi.org/10.1007/s10439-019-02380-4

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  • DOI: https://doi.org/10.1007/s10439-019-02380-4

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