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 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).