AI/ML

Methods for Gait Analysis During Obstacle Avoidance Task.




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Methods for Gait Analysis During Obstacle Avoidance Task.

Ann Biomed Eng. 2020 Feb;48(2):634-643

Authors: Patashov D, Menahem Y, Ben-Haim O, Gazit E, Maidan I, Mirelman A, Sosnik R, Goldstein D, Hausdorff JM

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.

PMID: 31598893 [PubMed - indexed for MEDLINE]

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