Recognition of motion patterns using accelerometers for ataxic gait assessment
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11150%2F21%3A10422642" target="_blank" >RIV/00216208:11150/21:10422642 - isvavai.cz</a>
Alternative codes found
RIV/60461373:22340/20:43921136 RIV/68407700:21730/21:00347475 RIV/00179906:_____/21:10422642
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=qHmpns7HMq" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=qHmpns7HMq</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00521-020-05103-2" target="_blank" >10.1007/s00521-020-05103-2</a>
Alternative languages
Result language
angličtina
Original language name
Recognition of motion patterns using accelerometers for ataxic gait assessment
Original language description
The recognition of motion patterns belongs to very important research areas related to neurology, rehabilitation, and robotics. It is based on modern sensor technologies and general mathematical methods, multidimensional signal processing, and machine learning. The present paper is devoted to the detection of features associated with accelerometric data acquired by 31 time-synchronized sensors located at different parts of the body. Experimental data sets were acquired from 25 individuals diagnosed as healthy controls and ataxic patients. The proposed method includes the application of the discrete Fourier transform for the estimation of the mean power in selected frequency bands and the use of these features for data segments classification. The study includes a comparison of results obtained from signals recorded at different positions. Evaluations are based on classification accuracy and cross-validation errors estimated by support vector machine, Bayesian, nearest neighbours (k-NN], and neural network (NN) methods. Results show that highest accuracies of 77.1%, 78.9%, 89.9%, 98.0%, and 98.5% were achieved by NN method for signals acquired from the sensors on the feet, legs, uplegs, shoulders, and head/spine, respectively, recorded in 201 signal segments. The entire study is based on observations in the clinical environment and suggests the importance of augmented reality to decisions and diagnosis in neurology.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
Project
<a href="/en/project/EF17_048%2F0007441" target="_blank" >EF17_048/0007441: PERSONMED - Center for the Development of Personalized Medicine in Age-Related Diseases</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Computing and Applications
ISSN
0941-0643
e-ISSN
—
Volume of the periodical
33
Issue of the periodical within the volume
7
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
2207-2215
UT code for WoS article
000543276900001
EID of the result in the Scopus database
2-s2.0-85087045961