@article{ref1,
title="Methods for gait analysis during obstacle avoidance task",
journal="Annals of biomedical engineering",
year="2019",
author="Patashov, Dmitry and Menahem, Yakir and Ben-Haim, Ohad and Gazit, Eran and Maidan, Inbal and Mirelman, Anat and Sosnik, Ronen and Goldstein, Dmitry and Hausdorff, Jeffrey M.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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.<p /> <p>Language: en</p>",
language="en",
issn="0090-6964",
doi="10.1007/s10439-019-02380-4",
url="http://dx.doi.org/10.1007/s10439-019-02380-4"
}