Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11150%2F21%3A10428922" target="_blank" >RIV/00216208:11150/21:10428922 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60461373:22340/21:43922527 RIV/00179906:_____/21:10428922 RIV/68407700:21730/21:00354817

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Kda8cVpxJR" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Kda8cVpxJR</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TNSRE.2021.3051093" target="_blank" >10.1109/TNSRE.2021.3051093</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Popis výsledku v původním jazyce

Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibilityof using accelerometricdata to optimise deep learning convolutional neural network systems to distinguish between ataxic and normal gait. The experimental dataset includes 860 signal segments of 16 ataxic patients and 19 individuals from the control set with the mean age of 38.6 and 39.6 years, respectively. The proposed methodology is based upon the analysis of frequency components of accelerometric signals simultaneously recorded at specific body positionswith a sampling frequencyof 60Hz. The deep learning system uses all of the frequency components in a range of &lt; 0, 30 &gt; Hz. Our classification results are compared with those obtained by standard methods, which include the support vector machine, Bayesian methods, and the two-layer neural network with features estimated as the relative power in selected frequency bands. Our results show that the appropriate selection of sensor positions can increase the accuracy from 81.2% for the foot position to 91.7% for the spine position. Combining the input data and the deep learning methodology with five layers increased the accuracy to 95.8%. Our methodology suggests that artificial intelligence methods and deep learning are efficient methods in the assessment of motion disorders and they have a wide range of further applications.

Název v anglickém jazyce

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Popis výsledku anglicky

Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibilityof using accelerometricdata to optimise deep learning convolutional neural network systems to distinguish between ataxic and normal gait. The experimental dataset includes 860 signal segments of 16 ataxic patients and 19 individuals from the control set with the mean age of 38.6 and 39.6 years, respectively. The proposed methodology is based upon the analysis of frequency components of accelerometric signals simultaneously recorded at specific body positionswith a sampling frequencyof 60Hz. The deep learning system uses all of the frequency components in a range of &lt; 0, 30 &gt; Hz. Our classification results are compared with those obtained by standard methods, which include the support vector machine, Bayesian methods, and the two-layer neural network with features estimated as the relative power in selected frequency bands. Our results show that the appropriate selection of sensor positions can increase the accuracy from 81.2% for the foot position to 91.7% for the spine position. Combining the input data and the deep learning methodology with five layers increased the accuracy to 95.8%. Our methodology suggests that artificial intelligence methods and deep learning are efficient methods in the assessment of motion disorders and they have a wide range of further applications.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Neural Systems and Rehabilitation Engineering

ISSN

1534-4320

e-ISSN

—

Svazek periodika

29

Číslo periodika v rámci svazku

MAR

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

360-367

Kód UT WoS článku

000626331500016

EID výsledku v databázi Scopus

2-s2.0-85099576318