Bayesian classification and analysis of gait disorders using image and depth sensors of Microsoft Kinect

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>

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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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

—

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2015

Confidentiality

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

Name of the periodical

Digital Signal Processing: A Review Journal

ISSN

1051-2004

e-ISSN

—

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