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A Brief Review on the Validity and Reliability of Microsoft Kinect Sensors for Functional Assessment ApplicationsDIAZ-MONTERROSAS, P. R.   , POSADA-GOMEZ, R. , MARTINEZ-SIBAJA, A. , AGUILAR-LASSERRE, A. A. , JUAREZ-MARTINEZ, U. , TRUJILLO-CABALLERO, J. C. |
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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
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. |
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[1] Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support, Örücü, Serkan, Selek, Murat, Applied Sciences, ISSN 2076-3417, Issue 2, Volume 10, 2020.
Digital Object Identifier: 10.3390/app10020611 [CrossRef]
[2] Pose and Optical Flow Fusion (POFF) for accurate tremor detection and quantification, Alper, Mehmet Akif, Goudreau, John, Daniel, Morris, Biocybernetics and Biomedical Engineering, ISSN 0208-5216, Issue 1, Volume 40, 2020.
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[3] Development of a human metabolic rate prediction model based on the use of Kinect-camera generated visual data-driven approaches, Na, HooSeung, Choi, Joon-Ho, Kim, HoSeong, Kim, Taeyeon, Building and Environment, ISSN 0360-1323, Issue , 2019.
Digital Object Identifier: 10.1016/j.buildenv.2019.106216 [CrossRef]
[4] Comparison of Azure Kinect and optical retroreflective motion capture for kinematic and spatiotemporal evaluation of the sit-to-stand test, Thomas, Jacob, Hall, Jamie B., Bliss, Rebecca, Guess, Trent M., Gait & Posture, ISSN 0966-6362, Issue , 2022.
Digital Object Identifier: 10.1016/j.gaitpost.2022.03.011 [CrossRef]
[5] A Comparative Analysis of Two Approaches for Estimation of Upper Limb Orientation Using Inertial and Kinect Sensors, ACHARYA, A., BHAT, S., KANTHI, M., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 22, 2022.
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[6] Depression Prevalence in Postgraduate Students and Its Association With Gait Abnormality, Fang, Jing, Wang, Tao, Li, Cancheng, Hu, Xiping, Ngai, Edith, Seet, Boon-Chong, Cheng, Jun, Guo, Yi, Jiang, Xin, IEEE Access, ISSN 2169-3536, Issue , 2019.
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[7] Kinect-Based Rehabilitation Systems for Stroke Patients: A Scoping Review, Almasi, Sohrab, Ahmadi, Hossein, Asadi, Farkhondeh, Shahmoradi, Leila, Arji, Goli, Alizadeh, Mojtaba, Kolivand, Hoshang, Kaya, Defne, BioMed Research International, ISSN 2314-6141, Issue , 2022.
Digital Object Identifier: 10.1155/2022/4339054 [CrossRef]
[8] Preliminary Viability Test of a 3-D-Consumer-Camera-Based System for Automatic Gait Feature Detection in People with and without Parkinson’s Disease, Arizpe-Gomez, Pedro, Harms, Kirsten, Fudickar, Sebastian, Janitzky, Kathrin, Witt, Karsten, Hein, Andreas, 2020 IEEE International Conference on Healthcare Informatics (ICHI), ISBN 978-1-7281-5382-7, 2020.
Digital Object Identifier: 10.1109/ICHI48887.2020.9374363 [CrossRef]
[9] Data Gathering Optimization in Wireless Sensor Networks Using Unmanned Aerial Vehicles, Vladuta, Valentin-Alexandru, Matei, Ioana, Bica, Ion, 2019 22nd International Conference on Control Systems and Computer Science (CSCS), ISBN 978-1-7281-2331-8, 2019.
Digital Object Identifier: 10.1109/CSCS.2019.00029 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
