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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

  • Czech description

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

Result continuities

  • Project

  • 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

  • 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