Predicting triplanar and bidirectional movements for a transtibial prosthesis for rehabilitation using intelligent neural networks.
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| Title: | Predicting triplanar and bidirectional movements for a transtibial prosthesis for rehabilitation using intelligent neural networks. |
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| Authors: | de la Cruz-Alejo, Jesus1 (AUTHOR) jdelacruz@tese.edu.mx, Lobato-Cadena, J. Antonio1 (AUTHOR), Arce-Vázquez, M. Belem1 (AUTHOR), Mora-Ortega, Agustin1 (AUTHOR) |
| Source: | Neural Computing & Applications. Apr2024, Vol. 36 Issue 11, p6085-6098. 14p. |
| Subjects: | Prosthetics, Myoelectric prosthesis, Intelligent networks, Human locomotion, Human mechanics, Artificial neural networks, Prosthesis design & construction |
| Abstract: | In this study, artificial neural networks (NN) are applied to the design of a transtibial prosthesis to adapt triplanar and bidirectional movements of human locomotion for rehabilitation. NN-based control is used because the prosthesis system is highly nonlinear and has variables with too many uncertainties caused by variations in ankle movements, weight damping, dorsiflexion, and flexion in the amputation area due to biological stimuli. To identify and detect these movements in the transtibial prosthesis, myoelectric signals are used that determine its position and adapt its trajectory through linear and rotary actuators. The input and desired parameters for the NN controller and the backpropagation algorithm are obtained based on the movements of the human ankle and foot based on their trajectory. The prototype is manufactured from different types of plastic using a 3D grapher, which can perform the main stages of human locomotion due to the learning carried out by the NN, reducing the risk of falls, and having a more comfortable and natural gait cycle in the rehabilitation of people. From the output response obtained from the NN controller, only a time delay is obtained without overshoot terms, and the trajectory tracking is adjusted. Simulation and experimental results show that the proposed NN-based control system can ensure the stability of the system and maintain good tracking of human locomotion. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 176080979 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting triplanar and bidirectional movements for a transtibial prosthesis for rehabilitation using intelligent neural networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22de+la+Cruz-Alejo%2C+Jesus%22">de la Cruz-Alejo, Jesus</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jdelacruz@tese.edu.mx</i><br /><searchLink fieldCode="AR" term="%22Lobato-Cadena%2C+J%2E+Antonio%22">Lobato-Cadena, J. Antonio</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Arce-Vázquez%2C+M%2E+Belem%22">Arce-Vázquez, M. Belem</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mora-Ortega%2C+Agustin%22">Mora-Ortega, Agustin</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Apr2024, Vol. 36 Issue 11, p6085-6098. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Prosthetics%22">Prosthetics</searchLink><br /><searchLink fieldCode="DE" term="%22Myoelectric+prosthesis%22">Myoelectric prosthesis</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+networks%22">Intelligent networks</searchLink><br /><searchLink fieldCode="DE" term="%22Human+locomotion%22">Human locomotion</searchLink><br /><searchLink fieldCode="DE" term="%22Human+mechanics%22">Human mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Prosthesis+design+%26+construction%22">Prosthesis design & construction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this study, artificial neural networks (NN) are applied to the design of a transtibial prosthesis to adapt triplanar and bidirectional movements of human locomotion for rehabilitation. NN-based control is used because the prosthesis system is highly nonlinear and has variables with too many uncertainties caused by variations in ankle movements, weight damping, dorsiflexion, and flexion in the amputation area due to biological stimuli. To identify and detect these movements in the transtibial prosthesis, myoelectric signals are used that determine its position and adapt its trajectory through linear and rotary actuators. The input and desired parameters for the NN controller and the backpropagation algorithm are obtained based on the movements of the human ankle and foot based on their trajectory. The prototype is manufactured from different types of plastic using a 3D grapher, which can perform the main stages of human locomotion due to the learning carried out by the NN, reducing the risk of falls, and having a more comfortable and natural gait cycle in the rehabilitation of people. From the output response obtained from the NN controller, only a time delay is obtained without overshoot terms, and the trajectory tracking is adjusted. Simulation and experimental results show that the proposed NN-based control system can ensure the stability of the system and maintain good tracking of human locomotion. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-023-09393-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 6085 Subjects: – SubjectFull: Prosthetics Type: general – SubjectFull: Myoelectric prosthesis Type: general – SubjectFull: Intelligent networks Type: general – SubjectFull: Human locomotion Type: general – SubjectFull: Human mechanics Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Prosthesis design & construction Type: general Titles: – TitleFull: Predicting triplanar and bidirectional movements for a transtibial prosthesis for rehabilitation using intelligent neural networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: de la Cruz-Alejo, Jesus – PersonEntity: Name: NameFull: Lobato-Cadena, J. Antonio – PersonEntity: Name: NameFull: Arce-Vázquez, M. Belem – PersonEntity: Name: NameFull: Mora-Ortega, Agustin IsPartOfRelationships: – BibEntity: Dates: – D: 20 M: 04 Text: Apr2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 36 – Type: issue Value: 11 Titles: – TitleFull: Neural Computing & Applications Type: main |
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