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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21730/15:00239784 RIV/60461373:22340/15:43899365

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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%.

Název v anglickém jazyce

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

Popis výsledku anglicky

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%.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Digital Signal Processing: A Review Journal

ISSN

1051-2004

e-ISSN

—

Svazek periodika

47

Číslo periodika v rámci svazku

neuvedeno

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

169-177

Kód UT WoS článku

000366072000014

EID výsledku v databázi Scopus

2-s2.0-84948083736