Publication:
Estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs

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dc.citedby0
dc.contributor.authorTham L.K.en_US
dc.contributor.authorAl Kouzbary M.en_US
dc.contributor.authorAl Kouzbary H.en_US
dc.contributor.authorLiu J.en_US
dc.contributor.authorAbu Osman N.A.en_US
dc.contributor.authorid36560464100en_US
dc.contributor.authorid57202956887en_US
dc.contributor.authorid57216612501en_US
dc.contributor.authorid57223432161en_US
dc.contributor.authorid8511221500en_US
dc.date.accessioned2024-10-14T03:17:25Z
dc.date.available2024-10-14T03:17:25Z
dc.date.issued2023
dc.description.abstractAssessment of the prosthetic gait is an important clinical approach to evaluate the quality and functionality of the prescribed lower limb prosthesis as well as to monitor rehabilitation progresses following limb amputation. Limited access to quantitative assessment tools generally affects the repeatability and consistency of prosthetic gait assessments in clinical practice. The rapidly developing wearable technology industry provides an alternative to objectively quantify prosthetic gait in the unconstrained environment. This study employs a neural network-based model in estimating three-dimensional body segmental orientation of the lower limb amputees during gait. Using a wearable system with inertial sensors attached to the lower limb segments, thirteen individuals with lower limb amputation performed two-minute walk tests on a robotic foot and a passive foot. The proposed model replicates features of a complementary filter to estimate drift free three-dimensional orientation of the intact and prosthetic limbs. The results indicate minimal estimation biases and high correlation, validating the ability of the proposed model to reproduce the properties of a complementary filter while avoiding the drawbacks, most notably in the transverse plane due to gravitational acceleration and magnetic disturbance. Results of this study also demonstrates the capability of the well-trained model to accurately estimate segmental orientation, regardless of amputation level, in different types of locomotion task. � 2023, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s13246-023-01332-6
dc.identifier.epage1739
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85174599215
dc.identifier.spage1723
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85174599215&doi=10.1007%2fs13246-023-01332-6&partnerID=40&md5=2def1056dd168e45213b4a2fc0c2412d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33908
dc.identifier.volume46
dc.pagecount16
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGreen Open Access
dc.relation.ispartofHybrid Gold Open Access
dc.sourceScopus
dc.sourcetitlePhysical and Engineering Sciences in Medicine
dc.subjectArtificial neural network
dc.subjectInertial sensors
dc.subjectNARX network
dc.subjectOrientation estimation
dc.subjectProsthetic gait
dc.subjectValidation
dc.subjectAmputation, Surgical
dc.subjectFoot
dc.subjectGait
dc.subjectHumans
dc.subjectLower Extremity
dc.subjectNeural Networks, Computer
dc.subjectAcceleration
dc.subjectArtificial limbs
dc.subjectInertial navigation systems
dc.subjectWearable sensors
dc.subjectAutoregressive neural networks
dc.subjectComplementary filters
dc.subjectExogenous input
dc.subjectInertial sensor
dc.subjectLower limb prosthesis
dc.subjectNARX network
dc.subjectOrientation estimation
dc.subjectProsthetic gait
dc.subjectSegmental orientation
dc.subjectValidation
dc.subjectacceleration
dc.subjectadult
dc.subjectamputee
dc.subjectArticle
dc.subjectartificial neural network
dc.subjectbiological model
dc.subjectcontrolled study
dc.subjectcorrelational study
dc.subjectgait
dc.subjectgravity
dc.subjecthuman
dc.subjecthuman experiment
dc.subjectleg amputation
dc.subjectlocomotion
dc.subjectmagnetism
dc.subjectmale
dc.subjectmeasurement accuracy
dc.subjectmiddle aged
dc.subjectnonlinear autoregressive neural network
dc.subjectnonlinear system
dc.subjectnormal human
dc.subjectreproducibility
dc.subjectrobotics
dc.subjectvalidation study
dc.subjectwalk test
dc.subjectamputation
dc.subjectartificial neural network
dc.subjectfoot
dc.subjectlower limb
dc.subjectNeural networks
dc.titleEstimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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