Dynamic reconfiguration for multi-magnet tracking in myokinetic prosthetic interfaces

Mendez, Sergio Andres Pertuz; Mendes, Davi de Alencar; Gherardini, Marta; Arboleda, Daniel Marinho Muñoz; Ayala, Helon Vicente Hultmann; Cipriani, Christian

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Campo DC	Valor	Idioma
dc.contributor.author	Mendez, Sergio Andres Pertuz	-
dc.contributor.author	Mendes, Davi de Alencar	-
dc.contributor.author	Gherardini, Marta	-
dc.contributor.author	Arboleda, Daniel Marinho Muñoz	-
dc.contributor.author	Ayala, Helon Vicente Hultmann	-
dc.contributor.author	Cipriani, Christian	-
dc.date.accessioned	2025-09-26T14:44:59Z	-
dc.date.available	2025-09-26T14:44:59Z	-
dc.date.issued	2024-09-19	-
dc.identifier.citation	MENDES, Sergio Andres Pertuz et al. Dynamic reconfiguration for multi-magnet tracking in myokinetic prosthetic interfaces. IEEE transactions on medical robotics and bionics, v. 6, n. 4, p. 1678-1687, 2024. DOI: 10.1109/TMRB.2024.3464093. Disponível em: https://ieeexplore.ieee.org/document/10684318. Acesso em: 25 ago. 2025.	pt_BR
dc.identifier.uri	http://repositorio.unb.br/handle/10482/52528	-
dc.language.iso	eng	pt_BR
dc.publisher	IEEE	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.title	Dynamic reconfiguration for multi-magnet tracking in myokinetic prosthetic interfaces	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Prótese	pt_BR
dc.subject.keyword	Aprendizado de máquina	pt_BR
dc.subject.keyword	Reconfiguração dinâmica parcial	pt_BR
dc.subject.keyword	Interface miocinética	pt_BR
dc.subject.keyword	Field Programmable Gate Arrays (FPGAs)	pt_BR
dc.identifier.doi	10.1109/TMRB.2024.3464093	pt_BR
dc.relation.publisherversion	https://ieeexplore.ieee.org/document/10684318	pt_BR
dc.description.abstract1	Recently myokinetic interfaces have been proposed to exploit magnet tracking for controlling bionic prostheses. This interface derives information about muscle contractions from permanent magnets implanted into the amputee’s forearm muscles. Machine learning models have been mapped on Field Programmable Gate Arrays (FPGAs) to track a single magnet, achieving good precision and computational efficiency, but consuming a large area and hardware resources. To track several magnets, here we propose a novel solution based on dynamic partial reconfiguration, switching three prediction models: a linear regressor, a radial basis function neural network, and a multi-layer perceptron neural network. A system with five magnets and 128 magnetic sensor inputs was used and experimental data were collected to train a system with five hardware predictors. To reduce the complexity of the models, we applied principal component analysis, ranking by correlation the number of inputs of each model. This run-time reconfigurable solution allows the circuits to be reconfigured in order to select the most reliable predictor model for each magnet while the rest of the circuit continues to operate extracting the most significant information from the captured signals. Thus, the proposed solution remarkably reduces the hardware occupation and improves the computational efficiency compared to previous solutions.	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-6311-3251	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-2156-5516	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-9219-6463	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0001-5406-3902	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0003-2108-0700	pt_BR
dc.contributor.affiliation	TU Dresden, Institute of Computer Engineering	pt_BR
dc.contributor.affiliation	University of Brasilia, Department of Mechanical Engineering	pt_BR
dc.contributor.affiliation	Scuola Superiore Sant’Anna, BioRobotics Institute	pt_BR
dc.contributor.affiliation	University of Brasilia, Department of Mechanical Engineering, Electronics Engineering Undergraduate Program	pt_BR
dc.contributor.affiliation	Pontifical Catholic University of Paraná, Department of Mechanical Engineering	pt_BR
dc.contributor.affiliation	Scuola Superiore Sant’Anna, BioRobotics Institute	pt_BR
dc.description.unidade	Faculdade de Tecnologia (FT)	pt_BR
dc.description.unidade	Departamento de Engenharia Mecânica (FT ENM)	pt_BR
dc.description.ppg	Programa de Pós-Graduação em Sistemas Mecatrônicos	pt_BR
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