Evaluation of Gait Disorders Using Accelerometric and Gyroscopic Data for Assessment of Neurological Diseases

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00179906%3A_____%2F25%3A10504820" target="_blank" >RIV/00179906:_____/25:10504820 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/25:00388071 RIV/00216208:11110/25:10504820 RIV/00216208:11150/25:10504820 RIV/70883521:28140/25:63596558 a 2 dalších

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=xka.K1mO9S" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=xka.K1mO9S</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3610159" target="_blank" >10.1109/ACCESS.2025.3610159</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of Gait Disorders Using Accelerometric and Gyroscopic Data for Assessment of Neurological Diseases

Popis výsledku v původním jazyce

Computational intelligence and digital signal processing are essential mathematical tools widely applied in biomedical and engineering domains. Gait symmetry analysis is particularly important for detecting motion disorders in neurology, rehabilitation, and sports science. This study presents a methodology for motion analysis using time-synchronized accelerometric and gyrometric sensors to capture dynamic gait patterns. Data were collected from 14 healthy controls and 17 individuals with Parkinson&apos;s disease-related gait impairments. The proposed approach integrates spectral analysis and digital filtering to remove noise and irrelevant frequency components during signal preprocessing. Motion classification is performed by analyzing energy distribution using discrete Fourier and wavelet transforms, enabling multilevel signal decomposition. Gait recognition-distinguishing between normal and abnormal patterns-is based on energy components in selected frequency bands and their ratios. Neural network classifiers achieved the highest performance, with a mean accuracy of 81.1% and a cross-validation error of 0.123, using data from sensors placed on the left and right sides of the body. Motion asymmetry detected by the model agreed with assessments of neurologists in 88% of cases. Results of this validation highlight the potential of frequency and scale domain analysis, digital signal processing, and artificial intelligence use in supporting the clinical diagnosis of Parkinson&apos;s disease and further neurological disorders.

Název v anglickém jazyce

Evaluation of Gait Disorders Using Accelerometric and Gyroscopic Data for Assessment of Neurological Diseases

Popis výsledku anglicky

Computational intelligence and digital signal processing are essential mathematical tools widely applied in biomedical and engineering domains. Gait symmetry analysis is particularly important for detecting motion disorders in neurology, rehabilitation, and sports science. This study presents a methodology for motion analysis using time-synchronized accelerometric and gyrometric sensors to capture dynamic gait patterns. Data were collected from 14 healthy controls and 17 individuals with Parkinson&apos;s disease-related gait impairments. The proposed approach integrates spectral analysis and digital filtering to remove noise and irrelevant frequency components during signal preprocessing. Motion classification is performed by analyzing energy distribution using discrete Fourier and wavelet transforms, enabling multilevel signal decomposition. Gait recognition-distinguishing between normal and abnormal patterns-is based on energy components in selected frequency bands and their ratios. Neural network classifiers achieved the highest performance, with a mean accuracy of 81.1% and a cross-validation error of 0.123, using data from sensors placed on the left and right sides of the body. Motion asymmetry detected by the model agreed with assessments of neurologists in 88% of cases. Results of this validation highlight the potential of frequency and scale domain analysis, digital signal processing, and artificial intelligence use in supporting the clinical diagnosis of Parkinson&apos;s disease and further neurological disorders.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

<a href="/cs/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotika a pokročilá průmyslová výroba</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

13

Číslo periodika v rámci svazku

SEP

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

163134-163142

Kód UT WoS článku

001579056200047

EID výsledku v databázi Scopus

2-s2.0-105016609279