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dc.contributor.authorKluwak, K.
dc.contributor.authorKlempous, R.
dc.contributor.authorChaczko, Z.
dc.contributor.authorRozenblit, J.W.
dc.contributor.authorKulbacki, M.
dc.date.accessioned2021-07-14T02:01:52Z
dc.date.available2021-07-14T02:01:52Z
dc.date.issued2021
dc.identifier.citationKluwak, K., Klempous, R., Chaczko, Z., Rozenblit, J. W., & Kulbacki, M. (2021). People lifting patterns—A reference dataset for practitioners. Sensors, 21(9).
dc.identifier.issn1424-8220
dc.identifier.doi10.3390/s21093142
dc.identifier.urihttp://hdl.handle.net/10150/660443
dc.description.abstractMany health professionals do not use correct person transfer techniques in their daily practice. This results in damage to the paraspinal musculature over time, resulting in lower back pain and injuries. In this work, we propose an approach for the accurate multimodal measurement of people lifting and related motion patterns for ergonomic education regarding the application of correct patient transfer techniques. Several examples of person lifting were recorded and processed through accurate instrumentation and the well-defined measurements of kinematics, kinetics, surface electromyography of muscles as well as multicamera video. This resulted in a complete measurement protocol and unique reference datasets of correct and incorrect lifting schemes for caregivers and patients. This understanding of multimodal motion patterns provides insights for further independent investigations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
dc.language.isoen
dc.publisherMDPI AG
dc.rightsCopyright © 2021 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectData processing tag detection
dc.subjectDecision support
dc.subjectErgonomics in people lifting
dc.subjectHuman motion dataset
dc.subjectHuman motion lab
dc.subjectMotion analysis
dc.subjectRecommending systems
dc.subjectTag detection
dc.titlePeople lifting patterns—a reference dataset for practitioners
dc.typeArticle
dc.typetext
dc.contributor.departmentDepartment of Electrical and Computer Engineering, University of Arizona
dc.contributor.departmentDepartment of Surgery, University of Arizona
dc.identifier.journalSensors
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleSensors
refterms.dateFOA2021-07-14T02:01:52Z


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Copyright © 2021 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as Copyright © 2021 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).