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

• Main Authors: Tham L.K., Al Kouzbary M., Al Kouzbary H., Liu J., Abu Osman N.A.
• Other Authors: 36560464100
• Format: Article
• Published: Springer Science and Business Media Deutschland GmbH 2024
• Subjects: 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; Segmental orientation; 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; 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 repea...