Methods for Gait Analysis During Obstacle Avoidance Task

Dmitry Patashov, Yakir Menahem, Ohad Ben-Haim, Eran Gazit, Inbal Maidan, Anat Mirelman, Ronen Sosnik, Dmitry Goldstein, Jeffrey M. Hausdorff

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish
Pages (from-to)634-643
Number of pages10
JournalAnnals of Biomedical Engineering
Issue number2
StatePublished - 1 Feb 2020
Externally publishedYes


  • Adaptive filtering
  • Complex envelope
  • Dual multi-label forecasting
  • Kernel clustering
  • Missing data
  • Noisy signal
  • Peak detection
  • Quasi-periodic signal
  • Signal segmentation

ASJC Scopus subject areas

  • Biomedical Engineering


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