Bayesian classification and analysis of gait disorders using image and depth sensors of Microsoft Kinect
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11150%2F15%3A10314679" target="_blank" >RIV/00216208:11150/15:10314679 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/15:00239784 RIV/60461373:22340/15:43899365
Result on the web
<a href="http://dx.doi.org/10.1016/j.dsp.2015.05.011" target="_blank" >http://dx.doi.org/10.1016/j.dsp.2015.05.011</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.dsp.2015.05.011" target="_blank" >10.1016/j.dsp.2015.05.011</a>
Alternative languages
Result language
angličtina
Original language name
Bayesian classification and analysis of gait disorders using image and depth sensors of Microsoft Kinect
Original language description
This paper presents a novel method of Bayesian gait recognition using Microsoft (MS) Kinect image and depth sensors and skeleton tracking in three-dimensional space. Although video sequences acquired by a complex camera system enable a very precise data analysis, it is possible to use much simpler technical devices to analyze video frames with sufficient accuracy for many applications. The use of the MS Kinect allows a simple 3-D modeling using its image and depth sensors for data acquisition, resulting in a matrix of 640 x 480 elements used for spatial modeling of a moving body. The experimental part of the paper is devoted to the study of three data sets: (i) 18 individuals with Parkinson's disease, (ii) 18 healthy agematched controls, and (iii) 15 trained young individuals forming the second reference set. The proposed algorithm involves methods for the estimation of the average stride length and gait speed of individuals in these sets. Digital signal processing methods and Bayesian probability classification algorithms are then used for gait feature analysis to recognize individuals suspected of having Parkinson's disease. The results include the estimation of the characteristics of selected gait features for patients with Parkinson's disease and for individuals from the reference sets, presentation of decision boundaries, and comparison of classification efficiency for different features. The achieved accuracy of the probabilistic classification was 94.1%.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Digital Signal Processing: A Review Journal
ISSN
1051-2004
e-ISSN
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Volume of the periodical
47
Issue of the periodical within the volume
neuvedeno
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
169-177
UT code for WoS article
000366072000014
EID of the result in the Scopus database
2-s2.0-84948083736