Quasi-Periodic and Noisy Signal's Processing Methods

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

Overview

Nowadays signal processing is a standard procedure for many fields, but it's not always an easy one. Some applications require dealing with noise and data loss issues that may significantly affect the desired results. This article provides methods to deal with those kinds of issues while using gait signals as an example.

Summary

We propose new accurate and robust methods for certain signal processing tasks that could significantly improve the results of data processing and analysis.

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Author Comments

Mr. Dmitry Patashov
Mr. Dmitry Patashov
Holon Institute of Technology
Lab. Director
Holon | Israel
Personally I enjoyed working with all of the co-authors and I hope that the developed methods will make their way into different signal processing sub-fields since most of them are general purpose data processing algorithms.Mr. Dmitry Patashov

Methods for Gait Analysis During Obstacle Avoidance Task.

Ann Biomed Eng 2020 Feb 9;48(2):634-643. Epub 2019 Oct 9.

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

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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Source
http://dx.doi.org/10.1007/s10439-019-02380-4DOI Listing
February 2020
1 Read
3.195 Impact Factor

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