Publication:
Estimation the center of pressure with bi-planar postural stability model and neural network system

dc.contributor.authorThunyanoot Prasertsakulen_US
dc.contributor.authorWarakorn Charoensuken_US
dc.contributor.otherMahidol Universityen_US
dc.date.accessioned2018-12-11T02:49:16Z
dc.date.accessioned2019-03-14T08:01:30Z
dc.date.available2018-12-11T02:49:16Z
dc.date.available2019-03-14T08:01:30Z
dc.date.issued2016-02-04en_US
dc.description.abstract© 2015 IEEE. Postural stability is a necessary activity for human livings. To understand this behavioral function can perform by using instruments or clinical assessments. Motion analysis is a famous method to analyze the motion pattern in healthy people or patients with dysfunctional movement which provide information in 3-dimension. To analyze the information can acquire from various methods and must compute with a specific software. Applying other methods, such as mathematical models, utilizes to describe mechanism of the postural stability in humans. The proposed study applies two mathematical models that explain the movement patterns in lateral and frontal planes for determination the position of center of pressure with the coordination in 3-dimension. The neural network model is used to estimate the position of center of pressure. The results from both systems indicate that these systems can be applied to estimate the position of center of pressure; however, the errors from mathematical models are lessor than another.en_US
dc.identifier.citationBMEiCON 2015 - 8th Biomedical Engineering International Conference. (2016)en_US
dc.identifier.doi10.1109/BMEiCON.2015.7399573en_US
dc.identifier.other2-s2.0-84969263287en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/123456789/40628
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84969263287&origin=inwarden_US
dc.subjectEngineeringen_US
dc.titleEstimation the center of pressure with bi-planar postural stability model and neural network systemen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84969263287&origin=inwarden_US

Files

Collections