Motion capture technology in industrial applications: A systematic review

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dc.contributor.author Menolotto, Matteo
dc.contributor.author Komaris, Dimitrios-Sokratis
dc.contributor.author Tedesco, Salvatore
dc.contributor.author O'Flynn, Brendan
dc.contributor.author Walsh, Michael
dc.date.accessioned 2020-10-09T12:19:10Z
dc.date.available 2020-10-09T12:19:10Z
dc.date.issued 2020-10-05
dc.identifier.citation Menolotto, M., Komaris, D.-S., Tedesco, S., O’Flynn, B. and Walsh, M. (2020) 'Motion Capture Technology in Industrial Applications: A Systematic Review', Sensors, 20(19), 5687 (25 pp). doi: 10.3390/s20195687 en
dc.identifier.volume 20 en
dc.identifier.issued 19 en
dc.identifier.endpage 1 en
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/10468/10647
dc.identifier.doi 10.3390/s20195687 en
dc.description.abstract The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition. en
dc.description.sponsorship Science Foundation Ireland (16/RC/3918 (CONFIRM), 12/RC/2289-P2 (INSIGHT), 13/RC/2077-CONNECT which are co-funded under the European Regional Development Fund) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher MDPI en
dc.relation.uri https://www.mdpi.com/1424-8220/20/19/5687
dc.rights © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. en
dc.rights.uri https://creativecommons.org/licenses/by/4.0/ en
dc.subject Health and safety en
dc.subject IMU en
dc.subject Industry 4.0 en
dc.subject Motion tracking en
dc.subject Robot control en
dc.subject Wearable sensors en
dc.title Motion capture technology in industrial applications: A systematic review en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Matteo Menolotto, Tyndall Micronano Electronics, University College Cork, Cork, Ireland. +353-21-490-3000 Email: matteo.menolotto@tyndall.ie en
dc.internal.availability Full text available en
dc.date.updated 2020-10-09T12:04:17Z
dc.description.version Published Version en
dc.internal.rssid 539598044
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder European Regional Development Fund en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Sensors en
dc.internal.copyrightchecked No
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress salvatore.tedesco@tyndall.ie en
dc.internal.IRISemailaddress matteo.menolotto@tyndall.ie en
dc.internal.IRISemailaddress sokratis.komaris@tyndall.ie en
dc.internal.IRISemailaddress brendan.oflynn@ucc.ie en
dc.internal.IRISemailaddress michael.walsh@ucc.ie en
dc.identifier.articleid 25 en
dc.internal.bibliocheck 5687 en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en


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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Except where otherwise noted, this item's license is described as © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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