Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F19%3A43917902" target="_blank" >RIV/60461373:22340/19:43917902 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/19:00339284 RIV/00179906:_____/19:10399923 RIV/00216208:11150/19:10399923 RIV/70883521:28140/19:63523864

Result on the web

<a href="https://www.mdpi.com/2073-8994/11/7/871" target="_blank" >https://www.mdpi.com/2073-8994/11/7/871</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/sym11070871" target="_blank" >10.3390/sym11070871</a>

Result language

angličtina

Original language name

Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

Original language description

Analysis of motion symmetry constitutes an important area with many applications in engineering, robotics, neurology and biomedicine. This paper presents the use of microelectromechanical sensors (MEMS), including accelerometers and gyrometers, to acquire data via mobile devices so as to monitor physical activities and their irregularities. Special attention is devoted to the analysis of the symmetry of the motion of the body when the same exercises are performed by the right and the left limb. The analyzed data include the motion of the legs on a home exercise bike under different levels of load. The method is based on signal analysis using the discrete wavelet transform and the evaluation of signal segment features such as the relative energy at selected decomposition levels. The subsequent classification of the evaluated features is performed by k-nearest neighbours, a Bayesian approach, a support vector machine, and neural networks. The highest average classification accuracy attained is 91.0% and the lowest mean cross-validation error is 0.091, resulting from the use of a neural network. This paper presents the advantages of the use of simple sensors, their combination and intelligent data processing for the numerical evaluation of motion features in the rehabilitation and monitoring of physical activities.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF17_048%2F0007441" target="_blank" >EF17_048/0007441: PERSONMED - Center for the Development of Personalized Medicine in Age-Related Diseases</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Symmetry

ISSN

2073-8994

e-ISSN

—

Volume of the periodical

11

Issue of the periodical within the volume

7

Country of publishing house

CH - SWITZERLAND

Number of pages

13

Pages from-to

"871-1"-"871-13"

UT code for WoS article

000481979000036

EID of the result in the Scopus database

2-s2.0-85068578952