Estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs
Assessment 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 repea...
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2024
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my.uniten.dspace-339082024-10-14T11:17:25Z Estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs Tham L.K. Al Kouzbary M. Al Kouzbary H. Liu J. Abu Osman N.A. 36560464100 57202956887 57216612501 57223432161 8511221500 Artificial neural network Inertial sensors NARX network Orientation estimation Prosthetic gait Validation Amputation, Surgical Foot Gait Humans Lower Extremity Neural Networks, Computer Acceleration Artificial limbs Inertial navigation systems Wearable sensors Autoregressive neural networks Complementary filters Exogenous input Inertial sensor Lower limb prosthesis NARX network Orientation estimation Prosthetic gait Segmental orientation Validation acceleration adult amputee Article artificial neural network biological model controlled study correlational study gait gravity human human experiment leg amputation locomotion magnetism male measurement accuracy middle aged nonlinear autoregressive neural network nonlinear system normal human reproducibility robotics validation study walk test amputation artificial neural network foot lower limb Neural networks Assessment 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). Final 2024-10-14T03:17:25Z 2024-10-14T03:17:25Z 2023 Article 10.1007/s13246-023-01332-6 2-s2.0-85174599215 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174599215&doi=10.1007%2fs13246-023-01332-6&partnerID=40&md5=2def1056dd168e45213b4a2fc0c2412d https://irepository.uniten.edu.my/handle/123456789/33908 46 4 1723 1739 All Open Access Green Open Access Hybrid Gold Open Access Springer Science and Business Media Deutschland GmbH Scopus |
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Artificial neural network Inertial sensors NARX network Orientation estimation Prosthetic gait Validation Amputation, Surgical Foot Gait Humans Lower Extremity Neural Networks, Computer Acceleration Artificial limbs Inertial navigation systems Wearable sensors Autoregressive neural networks Complementary filters Exogenous input Inertial sensor Lower limb prosthesis NARX network Orientation estimation Prosthetic gait Segmental orientation Validation acceleration adult amputee Article artificial neural network biological model controlled study correlational study gait gravity human human experiment leg amputation locomotion magnetism male measurement accuracy middle aged nonlinear autoregressive neural network nonlinear system normal human reproducibility robotics validation study walk test amputation artificial neural network foot lower limb Neural networks |
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Artificial neural network Inertial sensors NARX network Orientation estimation Prosthetic gait Validation Amputation, Surgical Foot Gait Humans Lower Extremity Neural Networks, Computer Acceleration Artificial limbs Inertial navigation systems Wearable sensors Autoregressive neural networks Complementary filters Exogenous input Inertial sensor Lower limb prosthesis NARX network Orientation estimation Prosthetic gait Segmental orientation Validation acceleration adult amputee Article artificial neural network biological model controlled study correlational study gait gravity human human experiment leg amputation locomotion magnetism male measurement accuracy middle aged nonlinear autoregressive neural network nonlinear system normal human reproducibility robotics validation study walk test amputation artificial neural network foot lower limb Neural networks Tham L.K. Al Kouzbary M. Al Kouzbary H. Liu J. Abu Osman N.A. Estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs |
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Assessment 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). |
| author2 |
36560464100 |
| author_facet |
36560464100 Tham L.K. Al Kouzbary M. Al Kouzbary H. Liu J. Abu Osman N.A. |
| format |
Article |
| author |
Tham L.K. Al Kouzbary M. Al Kouzbary H. Liu J. Abu Osman N.A. |
| author_sort |
Tham L.K. |
| title |
Estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs |
| title_short |
Estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs |
| title_full |
Estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs |
| title_fullStr |
Estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs |
| title_full_unstemmed |
Estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs |
| title_sort |
estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2024 |
| _version_ |
1814061030776504320 |
| score |
13.222552 |