Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/EF17_048%2F0007441" target="_blank" >EF17_048/0007441: PERSONMED - Centrum rozvoje personalizované medicíny u věkem podmíněných onemocnění</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Symmetry

ISSN

2073-8994

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

13

Strana od-do

"871-1"-"871-13"

Kód UT WoS článku

000481979000036

EID výsledku v databázi Scopus

2-s2.0-85068578952