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doi: 10.4316/AECE


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 HIGH-IMPACT PAPER 

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: 1,274 | Views: 4,383

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

[1] D. Regazzoni, G. De Vecchi, and C. Rizzi, "RGB cams vs RGB-D sensors?: Low cost motion capture technologies performances and limitations," Journal of Manufacturing Systems, vol. 33, no. 4, pp. 719–728, 2014.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 53]


[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]


[3] P. Plantard et al., "Pose Estimation with a Kinect for Ergonomic Studies: Evaluation of the Accuracy Using a Virtual Mannequin," Sensors, vol. 15, no. 1, pp. 1785–1803, Jan. 2015.
[CrossRef] [Web of Science Times Cited 76] [SCOPUS Times Cited 90]


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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 94] [SCOPUS Times Cited 114]


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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 493] [SCOPUS Times Cited 635]


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[CrossRef] [Web of Science Times Cited 458] [SCOPUS Times Cited 544]


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[CrossRef] [SCOPUS Times Cited 288]


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[CrossRef] [Web of Science Times Cited 249] [SCOPUS Times Cited 318]


[11] R. Lun and W. Zhao, "A Survey of Applications and Human Motion Recognition with Microsoft Kinect," International Journal of Pattern Recognition and Artificial Intelligence, vol. 29, no. 5, p. 1555008, Aug. 2015.
[CrossRef] [Web of Science Times Cited 181] [SCOPUS Times Cited 218]


[12] T. Mallick, P. P. Das, and A. K. Majumdar, "Characterizations of Noise in Kinect Depth Images: A Review," IEEE Sensors Journal, vol. 14, no. 6, pp. 1731–1740, Jun. 2014.
[CrossRef] [Web of Science Times Cited 101] [SCOPUS Times Cited 129]


[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 187] [SCOPUS Times Cited 272]


[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 49]


[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 269] [SCOPUS Times Cited 334]


[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 876] [SCOPUS Times Cited 1148]


[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 164] [SCOPUS Times Cited 182]


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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 87] [SCOPUS Times Cited 113]




References Weight

Web of Science® Citations for all references: 4,658 TCR
SCOPUS® Citations for all references: 6,050 TCR

Web of Science® Average Citations per reference: 155 ACR
SCOPUS® Average Citations per reference: 202 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 2024-06-25 09:32 in 196 seconds.




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