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JCR Impact Factor: 0.699
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  1/2018 - 16

A Brief Review on the Validity and Reliability of Microsoft Kinect Sensors for Functional Assessment Applications

DIAZ-MONTERROSAS, P. R. See more information about DIAZ-MONTERROSAS, P. R. on SCOPUS See more information about DIAZ-MONTERROSAS, P. R. on IEEExplore See more information about DIAZ-MONTERROSAS, P. R. on Web of Science, POSADA-GOMEZ, R. See more information about  POSADA-GOMEZ, R. on SCOPUS See more information about  POSADA-GOMEZ, R. on SCOPUS See more information about POSADA-GOMEZ, R. on Web of Science, MARTINEZ-SIBAJA, A. See more information about  MARTINEZ-SIBAJA, A. on SCOPUS See more information about  MARTINEZ-SIBAJA, A. on SCOPUS See more information about MARTINEZ-SIBAJA, A. on Web of Science, AGUILAR-LASSERRE, A. A. See more information about  AGUILAR-LASSERRE, A. A. on SCOPUS See more information about  AGUILAR-LASSERRE, A. A. on SCOPUS See more information about AGUILAR-LASSERRE, A. A. on Web of Science, JUAREZ-MARTINEZ, U. See more information about  JUAREZ-MARTINEZ, U. on SCOPUS See more information about  JUAREZ-MARTINEZ, U. on SCOPUS See more information about JUAREZ-MARTINEZ, U. on Web of Science, TRUJILLO-CABALLERO, J. C. See more information about TRUJILLO-CABALLERO, J. C. on SCOPUS See more information about TRUJILLO-CABALLERO, J. C. on SCOPUS See more information about TRUJILLO-CABALLERO, J. C. on Web of Science
 
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Download PDF pdficon (1,083 KB) | Citation | Downloads: 140 | Views: 323

Author keywords
computer vision, human computer interaction, pervasive computing, reviews, statistical analysis

References keywords
kinect(24), microsoft(10), validity(7), sensors(7), sensor(7), gait(7), recognition(6), measurement(6), posture(5), motion(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 131 - 136
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01016
Web of Science Accession Number: 000426449500016
SCOPUS ID: 85043275331

Abstract
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Full text preview
Kinect sensors are Human Computer Interaction devices oriented to entertainment, but have rapidly spread to several fields such as health care, physical therapy, and training. Their multiple advantages place them at present in a competitive situation compared to traditional solutions. On the other hand, their accuracy and precision for sensitive human applications are still under critical examination. This paper presents a brief literature review on the validity and reliability of the first and the second generation Kinect sensors to get an idea of the feasibility of their propagation as measuring devices in functional assessment applications. Results are difficult to compare because they depend largely on the type of measured elements, the angle of view of the measurement, the distance to the sensor, and even the diversity of human motion features. Nonetheless, they suggest that Kinect sensors are capable of properly identifying posture and motion, but not body or joint rotations, unusual postures, or occlusions.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 18]


[2] B. Bonnechère et al., "Validity and reliability of the Kinect within functional assessment activities: Comparison with standard stereophotogrammetry," Gait and Posture, vol. 39, no. 1, pp. 593–598, 2014.
[CrossRef] [Web of Science Times Cited 101] [SCOPUS Times Cited 118]


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[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 28]


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[CrossRef] [Full Text] [Web of Science Times Cited 17] [SCOPUS Times Cited 19]


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[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 9]


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[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 7]


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[CrossRef] [Web of Science Times Cited 211] [SCOPUS Times Cited 298]


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[CrossRef] [Web of Science Times Cited 255] [SCOPUS Times Cited 318]


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[CrossRef] [SCOPUS Times Cited 90]


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[CrossRef] [Web of Science Times Cited 118] [SCOPUS Times Cited 172]


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[CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 59]


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[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 46]


[13] P. K. Pisharady and M. Saerbeck, "Recent methods and databases in vision-based hand gesture recognition: A review," Computer Vision and Image Understanding, vol. 141, pp. 152–165, Dec. 2015.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 41]


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[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 12]


[15] D. Webster and O. Celik, "Systematic review of Kinect applications in elderly care and stroke rehabilitation," Journal of neuroengineering and rehabilitation, vol. 11, p. 108, 2014.
[CrossRef] [Web of Science Times Cited 87] [SCOPUS Times Cited 128]


[16] J. Han et al., "Enhanced computer vision with Microsoft Kinect sensor: A review," IEEE Transactions on Cybernetics, vol. 43, no. 5, pp. 1318–1334, 2013.
[CrossRef] [Web of Science Times Cited 433] [SCOPUS Times Cited 597]


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[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 17]


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[CrossRef] [Web of Science Times Cited 71] [SCOPUS Times Cited 83]


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[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 28]


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[CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 36]




References Weight

Web of Science® Citations for all references: 1,909 TCR
SCOPUS® Citations for all references: 2,612 TCR

Web of Science® Average Citations per reference: 64 ACR
SCOPUS® Average Citations per reference: 87 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-09-16 11:31 in 210 seconds.




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