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dc.contributor.author | Igual, Carles | es_ES |
dc.contributor.author | Igual García, Jorge | es_ES |
dc.date.accessioned | 2024-06-12T18:19:07Z | |
dc.date.available | 2024-06-12T18:19:07Z | |
dc.date.issued | 2024-05 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/205092 | |
dc.description.abstract | [EN] Machine learning-based controllers of prostheses using electromyographic signals have become very popular in the last decade. The regression approach allows a simultaneous and proportional control of the intended movement in a more natural way than the classification approach, where the number of movements is discrete by definition. However, it is not common to find regression-based controllers working for more than two degrees of freedom at the same time. In this paper, we present the application of the adaptive linear regressor in a relatively low-dimensional feature space with only eight sensors to the problem of a simultaneous and proportional control of three degrees of freedom (left¿right, up¿down and open¿close hand movements). We show that a key element usually overlooked in the learning process of the regressor is the training paradigm. We propose a closed-loop procedure, where the human learns how to improve the quality of the generated EMG signals, helping also to obtain a better controller. We apply it to 10 healthy and 3 limb-deficient subjects. Results show that the combination of the multidimensional targets and the open-loop training protocol significantly improve the performance, increasing the average completion rate from 53% to 65% for the most complicated case of simultaneously controlling the three degrees of freedom. | es_ES |
dc.description.sponsorship | This work is partially supported by Ministerio de Educacion, Cultura y Deporte (Spain) under grant FPU15/02870. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Sensors | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Electromyography | es_ES |
dc.subject | Adaptive filter | es_ES |
dc.subject | Prosthetics | es_ES |
dc.subject | Proportional control | es_ES |
dc.subject | Task analysis | es_ES |
dc.subject | Psychomotor performance | es_ES |
dc.subject | Computer-based training | es_ES |
dc.subject | Linear regression | es_ES |
dc.subject.classification | TEORÍA DE LA SEÑAL Y COMUNICACIONES | es_ES |
dc.title | Simultaneous Three-Degrees-of-Freedom Prosthetic Control Based on Linear Regression and Closed-Loop Training Protocol | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/s24103101 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MECD//FPU15%2F02870/ES/FPU15%2F02870/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació | es_ES |
dc.description.bibliographicCitation | Igual, C.; Igual García, J. (2024). Simultaneous Three-Degrees-of-Freedom Prosthetic Control Based on Linear Regression and Closed-Loop Training Protocol. Sensors. 24(10). https://doi.org/10.3390/s24103101 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/s24103101 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 24 | es_ES |
dc.description.issue | 10 | es_ES |
dc.identifier.eissn | 1424-8220 | es_ES |
dc.identifier.pmid | 38793955 | es_ES |
dc.identifier.pmcid | PMC11124855 | es_ES |
dc.relation.pasarela | S\518650 | es_ES |
dc.contributor.funder | Ministerio de Educación, Cultura y Deporte | es_ES |