Motion capture technology in industrial applications: A systematic review

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dc.contributor.authorMenolotto, Matteo
dc.contributor.authorKomaris, Dimitrios-Sokratis
dc.contributor.authorTedesco, Salvatore
dc.contributor.authorO'Flynn, Brendan
dc.contributor.authorWalsh, Michael
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2020-10-09T12:19:10Z
dc.date.available2020-10-09T12:19:10Z
dc.date.issued2020-10-05
dc.date.updated2020-10-09T12:04:17Z
dc.description.abstractThe 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.sponsorshipScience 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.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid25en
dc.identifier.citationMenolotto, 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/s20195687en
dc.identifier.doi10.3390/s20195687en
dc.identifier.endpage1en
dc.identifier.issn1424-8220
dc.identifier.issued19en
dc.identifier.journaltitleSensorsen
dc.identifier.urihttps://hdl.handle.net/10468/10647
dc.identifier.volume20en
dc.language.isoenen
dc.publisherMDPIen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://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.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectHealth and safetyen
dc.subjectIMUen
dc.subjectIndustry 4.0en
dc.subjectMotion trackingen
dc.subjectRobot controlen
dc.subjectWearable sensorsen
dc.titleMotion capture technology in industrial applications: A systematic reviewen
dc.typeArticle (peer-reviewed)en
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