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Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion

Transhumeral amputees experience considerable difficulties with controlling a multifunctional prosthesis (powered hand, wrist, and elbow) due to the lack of available muscles to provide electromyographic (EMG) signals. The residual limb motion strategy has become a popular alternative for transhumer...

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Autores principales: Ahmed, Muhammad Hannan, Chai, Jiazheng, Shimoda, Shingo, Hayashibe, Mitsuhiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181452/
https://www.ncbi.nlm.nih.gov/pubmed/37177396
http://dx.doi.org/10.3390/s23094188
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author Ahmed, Muhammad Hannan
Chai, Jiazheng
Shimoda, Shingo
Hayashibe, Mitsuhiro
author_facet Ahmed, Muhammad Hannan
Chai, Jiazheng
Shimoda, Shingo
Hayashibe, Mitsuhiro
author_sort Ahmed, Muhammad Hannan
collection PubMed
description Transhumeral amputees experience considerable difficulties with controlling a multifunctional prosthesis (powered hand, wrist, and elbow) due to the lack of available muscles to provide electromyographic (EMG) signals. The residual limb motion strategy has become a popular alternative for transhumeral prosthesis control. It provides an intuitive way to estimate the motion of the prosthesis based on the residual shoulder motion, especially for target reaching tasks. Conventionally, a predictive model, typically an artificial neural network (ANN), is directly trained and relied upon to map the shoulder–elbow kinematics using the data from able-bodied subjects without extracting any prior synergistic information. However, it is essential to explicitly identify effective synergies and make them transferable across amputee users for higher accuracy and robustness. To overcome this limitation of the conventional ANN learning approach, this study explicitly combines the kinematic synergies with a recurrent neural network (RNN) to propose a synergy-space neural network for estimating forearm motions (i.e., elbow joint flexion–extension and pronation–supination angles) based on residual shoulder motions. We tested 36 training strategies for each of the 14 subjects, comparing the proposed synergy-space and conventional neural network learning approaches, and we statistically evaluated the results using Pearson’s correlation method and the analysis of variance (ANOVA) test. The offline cross-subject analysis indicates that the synergy-space neural network exhibits superior robustness to inter-individual variability, demonstrating the potential of this approach as a transferable and generalized control strategy for transhumeral prosthesis control.
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spelling pubmed-101814522023-05-13 Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion Ahmed, Muhammad Hannan Chai, Jiazheng Shimoda, Shingo Hayashibe, Mitsuhiro Sensors (Basel) Article Transhumeral amputees experience considerable difficulties with controlling a multifunctional prosthesis (powered hand, wrist, and elbow) due to the lack of available muscles to provide electromyographic (EMG) signals. The residual limb motion strategy has become a popular alternative for transhumeral prosthesis control. It provides an intuitive way to estimate the motion of the prosthesis based on the residual shoulder motion, especially for target reaching tasks. Conventionally, a predictive model, typically an artificial neural network (ANN), is directly trained and relied upon to map the shoulder–elbow kinematics using the data from able-bodied subjects without extracting any prior synergistic information. However, it is essential to explicitly identify effective synergies and make them transferable across amputee users for higher accuracy and robustness. To overcome this limitation of the conventional ANN learning approach, this study explicitly combines the kinematic synergies with a recurrent neural network (RNN) to propose a synergy-space neural network for estimating forearm motions (i.e., elbow joint flexion–extension and pronation–supination angles) based on residual shoulder motions. We tested 36 training strategies for each of the 14 subjects, comparing the proposed synergy-space and conventional neural network learning approaches, and we statistically evaluated the results using Pearson’s correlation method and the analysis of variance (ANOVA) test. The offline cross-subject analysis indicates that the synergy-space neural network exhibits superior robustness to inter-individual variability, demonstrating the potential of this approach as a transferable and generalized control strategy for transhumeral prosthesis control. MDPI 2023-04-22 /pmc/articles/PMC10181452/ /pubmed/37177396 http://dx.doi.org/10.3390/s23094188 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ahmed, Muhammad Hannan
Chai, Jiazheng
Shimoda, Shingo
Hayashibe, Mitsuhiro
Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion
title Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion
title_full Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion
title_fullStr Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion
title_full_unstemmed Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion
title_short Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion
title_sort synergy-space recurrent neural network for transferable forearm motion prediction from residual limb motion
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181452/
https://www.ncbi.nlm.nih.gov/pubmed/37177396
http://dx.doi.org/10.3390/s23094188
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