Evaluation of Gait Disorders Using Accelerometric and Gyroscopic Data for Assessment of Neurological Diseases
The result's identifiers
Result code in 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>
Alternative codes found
RIV/68407700:21730/25:00388071 RIV/00216208:11110/25:10504820 RIV/00216208:11150/25:10504820 RIV/70883521:28140/25:63596558 and 2 more
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Evaluation of Gait Disorders Using Accelerometric and Gyroscopic Data for Assessment of Neurological Diseases
Original language description
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'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's disease and further neurological disorders.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
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
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
13
Issue of the periodical within the volume
SEP
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
163134-163142
UT code for WoS article
001579056200047
EID of the result in the Scopus database
2-s2.0-105016609279