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Generating an adaptive and robust walking pattern for a prosthetic ankle-foot by utilizing a nonlinear autoregressive network with exogenous inputs

One of the major challenges in developing powered lower limb prostheses is emulating the behavior of an intact lower limb with different walking speeds over diverse terrains. Numerous studies have been conducted on control algorithms in the field of rehabilitation robotics to achieve this overarchin...

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Main Authors: Al Kouzbary, Hamza, Al Kouzbary, Mouaz, Tham, Lai Kuan, Liu, Jingjing, Shasmin, Hanie Nadia, Abu Osman, Noor Azuan
Format: Article
Published: Institute of Electrical and Electronics Engineers (IEEE) 2022
Subjects:
QA75 Electronic computers. Computer science
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.um.edu.my/41028/
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spelling my.um.eprints.410282023-08-30T04:01:20Z http://eprints.um.edu.my/41028/ Generating an adaptive and robust walking pattern for a prosthetic ankle-foot by utilizing a nonlinear autoregressive network with exogenous inputs Al Kouzbary, Hamza Al Kouzbary, Mouaz Tham, Lai Kuan Liu, Jingjing Shasmin, Hanie Nadia Abu Osman, Noor Azuan QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering One of the major challenges in developing powered lower limb prostheses is emulating the behavior of an intact lower limb with different walking speeds over diverse terrains. Numerous studies have been conducted on control algorithms in the field of rehabilitation robotics to achieve this overarching goal. Recent studies on powered prostheses have frequently used a hierarchical control scheme consisting of three control levels. Most control structures have at least one element of discrete transition properties that requires numerous sensors to improve classification accuracy, consequently increasing computational load and costs. In this study, we proposed a user-independent and free-mode method for eliminating the need to switch among different controllers. We constructed a database by using four OPAL wearable devices (Mobility Lab, APDM Inc., USA) for seven able-bodied subjects. We recorded the gait of each subject at three ambulation speeds during ground-level walking to train a nonlinear autoregressive network with an exogenous input recurrent neural network (NARX RNN) to estimate foot orientation (angular position) in the sagittal plane using shank angular velocity as external input. The trained NARX RNN estimated the foot orientation of all the subjects at different walking speeds over flat terrain with an average root-mean-square error (RMSE) of 2.1 degrees +/- 1.7 degrees. The minimum correlation between the estimated and measured values was 86%. Moreover, a t-test showed that the error was normally distributed with a high certainty level (0.88 minimum p-value). Institute of Electrical and Electronics Engineers (IEEE) 2022-11 Article PeerReviewed Al Kouzbary, Hamza and Al Kouzbary, Mouaz and Tham, Lai Kuan and Liu, Jingjing and Shasmin, Hanie Nadia and Abu Osman, Noor Azuan (2022) Generating an adaptive and robust walking pattern for a prosthetic ankle-foot by utilizing a nonlinear autoregressive network with exogenous inputs. IEEE Transactions on Neural Networks and Learning Systems, 33 (11). pp. 6297-6305. ISSN 2162-237X, DOI https://doi.org/10.1109/TNNLS.2021.3076060 <https://doi.org/10.1109/TNNLS.2021.3076060>. 10.1109/TNNLS.2021.3076060
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QA75 Electronic computers. Computer science
TK Electrical engineering. Electronics Nuclear engineering
Al Kouzbary, Hamza
Al Kouzbary, Mouaz
Tham, Lai Kuan
Liu, Jingjing
Shasmin, Hanie Nadia
Abu Osman, Noor Azuan
Generating an adaptive and robust walking pattern for a prosthetic ankle-foot by utilizing a nonlinear autoregressive network with exogenous inputs
description One of the major challenges in developing powered lower limb prostheses is emulating the behavior of an intact lower limb with different walking speeds over diverse terrains. Numerous studies have been conducted on control algorithms in the field of rehabilitation robotics to achieve this overarching goal. Recent studies on powered prostheses have frequently used a hierarchical control scheme consisting of three control levels. Most control structures have at least one element of discrete transition properties that requires numerous sensors to improve classification accuracy, consequently increasing computational load and costs. In this study, we proposed a user-independent and free-mode method for eliminating the need to switch among different controllers. We constructed a database by using four OPAL wearable devices (Mobility Lab, APDM Inc., USA) for seven able-bodied subjects. We recorded the gait of each subject at three ambulation speeds during ground-level walking to train a nonlinear autoregressive network with an exogenous input recurrent neural network (NARX RNN) to estimate foot orientation (angular position) in the sagittal plane using shank angular velocity as external input. The trained NARX RNN estimated the foot orientation of all the subjects at different walking speeds over flat terrain with an average root-mean-square error (RMSE) of 2.1 degrees +/- 1.7 degrees. The minimum correlation between the estimated and measured values was 86%. Moreover, a t-test showed that the error was normally distributed with a high certainty level (0.88 minimum p-value).
format Article
author Al Kouzbary, Hamza
Al Kouzbary, Mouaz
Tham, Lai Kuan
Liu, Jingjing
Shasmin, Hanie Nadia
Abu Osman, Noor Azuan
author_facet Al Kouzbary, Hamza
Al Kouzbary, Mouaz
Tham, Lai Kuan
Liu, Jingjing
Shasmin, Hanie Nadia
Abu Osman, Noor Azuan
author_sort Al Kouzbary, Hamza
title Generating an adaptive and robust walking pattern for a prosthetic ankle-foot by utilizing a nonlinear autoregressive network with exogenous inputs
title_short Generating an adaptive and robust walking pattern for a prosthetic ankle-foot by utilizing a nonlinear autoregressive network with exogenous inputs
title_full Generating an adaptive and robust walking pattern for a prosthetic ankle-foot by utilizing a nonlinear autoregressive network with exogenous inputs
title_fullStr Generating an adaptive and robust walking pattern for a prosthetic ankle-foot by utilizing a nonlinear autoregressive network with exogenous inputs
title_full_unstemmed Generating an adaptive and robust walking pattern for a prosthetic ankle-foot by utilizing a nonlinear autoregressive network with exogenous inputs
title_sort generating an adaptive and robust walking pattern for a prosthetic ankle-foot by utilizing a nonlinear autoregressive network with exogenous inputs
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2022
url http://eprints.um.edu.my/41028/
_version_ 1776247431915110400
score 13.211869

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