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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The quality of modern measuring instruments has a strong influence on the speed of diagnosing diseases of the human musculoskeletal system. The research is focused on automatization of the method of gait analysis. The study involved six healthy subjects. The subjects walk straight. Each subject made several gait types: casual walking and imitation of a non-standard gait, including shuffling, lameness, clubfoot, walking from the heel, rolling from heel to toe, walking with hands in pockets, and catwalk. Each type of gait was recorded three times. For video fixation, the Vicon Nexus system was used. A total of 27 reflective markers were placed on the special anatomical regions. The goniometry methods were used. The walk data were divided by steps and by step phases. Kinematic parameters for estimation were formulated and calculated. An approach for data clusterization is presented. For this purpose, angle data were interpolated and the interpolation coefficients were used for clustering the data. The data were processed and four cluster groups were found. Typical angulograms for cluster groups were presented. For each group, average angles were calculated. A statistically significant difference was found between received cluster groups.

Details

Title
The Automatization of the Gait Analysis by the Vicon Video System: A Pilot Study
Author
Smirnova, Victoriya 1 ; Khamatnurova, Regina 2 ; Kharin, Nikita 3   VIAFID ORCID Logo  ; Yaikova, Elena 4 ; Baltina, Tatiana 5   VIAFID ORCID Logo  ; Sachenkov, Oskar 6   VIAFID ORCID Logo 

 Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 420008 Kazan, Russia; N.I. Lobachevsky Institute of Mathematics and Mechanics, Kazan Federal University, 420008 Kazan, Russia 
 Interdisciplinary Neuroscience Faculty, Goethe-Universität Frankfurt am Main, 60323 Frankfurt am Main, Germany 
 N.I. Lobachevsky Institute of Mathematics and Mechanics, Kazan Federal University, 420008 Kazan, Russia; Institute of Engineering, Kazan Federal University, 420008 Kazan, Russia 
 Neurosurgical Department, Central City Clinical Hospital, 432017 Ulyanovsk, Russia 
 Institute of Fundamental Medicine and Biology, Kazan Federal University, 420008 Kazan, Russia 
 N.I. Lobachevsky Institute of Mathematics and Mechanics, Kazan Federal University, 420008 Kazan, Russia; Department Machines Science and Engineering Graphics, Tupolev Kazan National Research Technical University, 420111 Kazan, Russia 
First page
7178
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2724310398
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.