Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning

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dc.contributor.authorLim, Hyerimko
dc.contributor.authorKim, Bumjoonko
dc.contributor.authorPark, Sukyungko
dc.date.accessioned2020-03-19T02:21:12Z-
dc.date.available2020-03-19T02:21:12Z-
dc.date.created2020-02-18-
dc.date.created2020-02-18-
dc.date.issued2020-01-
dc.identifier.citationSENSORS, v.20, no.1-
dc.identifier.issn1424-8220-
dc.identifier.urihttp://hdl.handle.net/10203/272589-
dc.description.abstractRecent studies have reported the application of artificial neural network (ANN) techniques on data of inertial measurement units (IMUs) to predict ground reaction forces (GRFs), which could serve as quantitative indicators of sports performance or rehabilitation. The number of IMUs and their measurement locations are often determined heuristically, and the rationale underlying the selection of these parameter values is not discussed. Using the dynamic relationship between the center of mass (CoM), the GRFs and joint kinetics, we propose the CoM as a single measurement location with which to predict the dynamic data of the lower limbs, using an ANN. Data from seven subjects walking on a treadmill at various speeds were collected from a single IMU worn near the sacrum. The data was segmented by step and numerically processed for integration. Six segment angles of the stance and swing leg, three joint torques, and two GRFs were estimated from the kinematics of the CoM measured from a single IMU sensor, with fair accuracy. These results indicate the importance of the CoM as a dynamic determinant of multi-segment kinetics during walking. The tradeoff between data quantity and wearable convenience can be solved by utilizing a machine learning algorithm based on the dynamic characteristics of human walking.-
dc.languageEnglish-
dc.publisherMDPI-
dc.titlePrediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning-
dc.typeArticle-
dc.identifier.wosid000510493100130-
dc.identifier.scopusid2-s2.0-85077290698-
dc.type.rimsART-
dc.citation.volume20-
dc.citation.issue1-
dc.citation.publicationnameSENSORS-
dc.identifier.doi10.3390/s20010130-
dc.contributor.localauthorPark, Sukyung-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorbiomechanics-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthorwalking-
dc.subject.keywordAuthorground reaction forces-
dc.subject.keywordAuthorjoint torques-
dc.subject.keywordAuthorspring mechanics-
dc.subject.keywordAuthorwearables-
dc.subject.keywordPlusGROUND REACTION FORCES-
dc.subject.keywordPlusARTIFICIAL NEURAL-NETWORKS-
dc.subject.keywordPlusGLOBAL OPTIMIZATION-
dc.subject.keywordPlusMECHANICAL WORK-
dc.subject.keywordPlusGAIT-
dc.subject.keywordPlusMODEL-
dc.subject.keywordPlusBEHAVIOR-
dc.subject.keywordPlusIMPULSE-
dc.subject.keywordPlusINJURY-
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