Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/74584
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dc.contributor.authorKittichai Wantanajittikulen_US
dc.contributor.authorChakrit Wiboonsuntharangkoonen_US
dc.contributor.authorBusaba Chuatrakoonen_US
dc.contributor.authorSiriphan Kongsawasdien_US
dc.date.accessioned2022-10-16T06:44:47Z-
dc.date.available2022-10-16T06:44:47Z-
dc.date.issued2022-01-01en_US
dc.identifier.issn1537744Xen_US
dc.identifier.other2-s2.0-85133253799en_US
dc.identifier.other10.1155/2022/9483665en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85133253799&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/74584-
dc.description.abstractPostural sway indicates controlling stability in response to standing balance perturbations and determines risk of falling. In order to assess balance and postural sway, costly laboratory equipment is required, making it impractical for clinical settings. The study aimed to develop a triaxial inertial sensor and apply machine learning (ML) algorithms for predicting trajectory of the center of pressure (COP) path of postural sway. Fifty-three healthy adults, with a mean age of 46 years, participated. The inertial sensor prototype was investigated for its concurrent validity relative to the COP path length obtained from the force platform measurement. Then, ML was applied to predict the COP path by using sensor-sway metrics as the input. The results of the study revealed that all variables from the sensor prototype demonstrated high concurrent validity against the COP path from the force platform measurement (ρ > 0.75; p < 0.001). The agreement between sway metrics, derived from the sensor and ML algorithms, illustrated good to excellent agreement (ICC; 0.89-0.95) between COP paths from the sensor metrics, with respect to the force plate measurement. This study demonstrated that the inertial sensor, in comparison to the standard tool, would be an option for balance assessment since it is of low-cost, conveniently portable, and comparable to the accuracy of standard force platform measurement.en_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectEnvironmental Scienceen_US
dc.titleApplication of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial Sensoren_US
dc.typeJournalen_US
article.title.sourcetitleTheScientificWorldJournalen_US
article.volume2022en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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