Computational Intelligence and Wavelet Transform in Walk Symmetry Analysis Using Accelerometers

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F25%3A63598838" target="_blank" >RIV/70883521:28140/25:63598838 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/25:00388076 RIV/00216208:11150/25:10513188 RIV/60461373:22340/25:43932963

Result on the web

<a href="https://ieeexplore.ieee.org/document/11075087" target="_blank" >https://ieeexplore.ieee.org/document/11075087</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/DSP65409.2025.11075087" target="_blank" >10.1109/DSP65409.2025.11075087</a>

Result language

angličtina

Original language name

Computational Intelligence and Wavelet Transform in Walk Symmetry Analysis Using Accelerometers

Original language description

Computational intelligence and digital signal processing methods are fundamental mathematical tools widely used in biomedical and engineering applications. Gait symmetry analysis plays a very important role in detecting motion disorders in neurology, rehabilitation, and sports performance. This study focuses on data acquisition using a set of accelerometric sensors to record motion dynamics, ensure time synchronization of signals, and extract features for recognizing distinct motion patterns. The proposed methodology incorporates spectral analysis and digital filtering techniques to eliminate noise and irrelevant frequency components. Motion symmetry analysis is performed using energy distribution, calculated by discrete Fourier and wavelet transforms, with a focus on detailed coefficients at a specified decomposition level. Symmetry estimation is achieved by analyzing the ratio of energy within wavelet bands corresponding to the left and right sides of the body. Furthermore, spatial pattern distribution is analyzed to identify motion asymmetry, with artificial intelligence techniques employed for its evaluation. These results demonstrate the potential of computational intel-ligence in clinical diagnostics of specific neurological disorders.

Czech name

—

Czech description

—

Type

D - Article in proceedings

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/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Article name in the collection

2025 25TH INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING, DSP

ISBN

979-8-3315-1214-9

ISSN

1546-1874

e-ISSN

2165-3577

Number of pages

4

Pages from-to

1-4

Publisher name

IEEE

Place of publication

New York

Event location

Pylos

Event date

Jun 25, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001556221900068