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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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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: 1,245 | Views: 4,219

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 34] [SCOPUS Times Cited 50]


[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 185] [SCOPUS Times Cited 212]


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[CrossRef] [Web of Science Times Cited 75] [SCOPUS Times Cited 89]


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[CrossRef] [Full Text] [Web of Science Times Cited 28] [SCOPUS Times Cited 28]


[5] B. Müller et al., "Validation of enhanced kinect sensor based motion capturing for gait assessment," PLOS ONE, vol. 12, no. 4, p. e0175813, Apr. 2017.
[CrossRef] [Web of Science Times Cited 87] [SCOPUS Times Cited 108]


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[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 31]


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[CrossRef] [Web of Science Times Cited 490] [SCOPUS Times Cited 629]


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[CrossRef] [Web of Science Times Cited 453] [SCOPUS Times Cited 540]


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[CrossRef] [SCOPUS Times Cited 285]


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[CrossRef] [Web of Science Times Cited 247] [SCOPUS Times Cited 315]


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[CrossRef] [Web of Science Times Cited 177] [SCOPUS Times Cited 214]


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[CrossRef] [Web of Science Times Cited 99] [SCOPUS Times Cited 125]


[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 185] [SCOPUS Times Cited 265]


[14] T. D'Orazio et al., "Recent trends in gesture recognition: how depth data has improved classical approaches," Image and Vision Computing, vol. 52, pp. 56–72, Aug. 2016.
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 48]


[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 265] [SCOPUS Times Cited 330]


[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 869] [SCOPUS Times Cited 1136]


[17] T. Breuer, C. Bodensteiner, and M. Arens, "Low-cost commodity depth sensor comparison and accuracy analysis," in Electro-Optical Remote Sensing, Photonic Technologies, and Applications, 2014, vol. 9250, p. 92500G.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 36]


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[CrossRef] [Web of Science Times Cited 160] [SCOPUS Times Cited 180]


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[20] B. Galna et al., "Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease," Gait & posture, vol. 39, no. 4, pp. 1062–8, Apr. 2014.
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[CrossRef] [Web of Science Times Cited 81] [SCOPUS Times Cited 99]


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[CrossRef] [Web of Science Times Cited 86] [SCOPUS Times Cited 109]




References Weight

Web of Science® Citations for all references: 4,601 TCR
SCOPUS® Citations for all references: 5,971 TCR

Web of Science® Average Citations per reference: 153 ACR
SCOPUS® Average Citations per reference: 199 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 2024-03-29 06:27 in 225 seconds.




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