Desarrollo de estrategias de control biocooperativo para plataformas robotizadas de rehabilitación neuromotora

Rehabilitation robotics has emerged as a promising solution to promote motor recovery and functional independence for patients with neurological disorders. Neurorehabilitation with robotic devices has demonstrated its potential to provide intensive and repetitive training to promote motor recovery....

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Autor: Cisnal De La Rica, Ana
Tipo de recurso: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/62670
Acceso en línea:https://doi.org/10.35376/10324/62670
https://uvadoc.uva.es/handle/10324/62670
Access Level:acceso abierto
Palabra clave:Robotics
Robótica
Neurorehabilitation
Neurorrehabilitación
Biocooperative
Biocooperativo
Control
1207.02 Sistemas de Control
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network_name_str España
repository_id_str
dc.title.none.fl_str_mv Desarrollo de estrategias de control biocooperativo para plataformas robotizadas de rehabilitación neuromotora
title Desarrollo de estrategias de control biocooperativo para plataformas robotizadas de rehabilitación neuromotora
spellingShingle Desarrollo de estrategias de control biocooperativo para plataformas robotizadas de rehabilitación neuromotora
Cisnal De La Rica, Ana
Robotics
Robotics
Robótica
Neurorehabilitation
Neurorrehabilitación
Biocooperative
Biocooperativo
Control
Control
1207.02 Sistemas de Control
title_short Desarrollo de estrategias de control biocooperativo para plataformas robotizadas de rehabilitación neuromotora
title_full Desarrollo de estrategias de control biocooperativo para plataformas robotizadas de rehabilitación neuromotora
title_fullStr Desarrollo de estrategias de control biocooperativo para plataformas robotizadas de rehabilitación neuromotora
title_full_unstemmed Desarrollo de estrategias de control biocooperativo para plataformas robotizadas de rehabilitación neuromotora
title_sort Desarrollo de estrategias de control biocooperativo para plataformas robotizadas de rehabilitación neuromotora
dc.creator.none.fl_str_mv Cisnal De La Rica, Ana
author Cisnal De La Rica, Ana
author_facet Cisnal De La Rica, Ana
author_role author
dc.contributor.none.fl_str_mv Fraile Marinero, Juan Carlos
Pérez Turiel, Javier
Universidad de Valladolid. Escuela de Doctorado
dc.subject.none.fl_str_mv Robotics
Robotics
Robótica
Neurorehabilitation
Neurorrehabilitación
Biocooperative
Biocooperativo
Control
Control
1207.02 Sistemas de Control
topic Robotics
Robotics
Robótica
Neurorehabilitation
Neurorrehabilitación
Biocooperative
Biocooperativo
Control
Control
1207.02 Sistemas de Control
description Rehabilitation robotics has emerged as a promising solution to promote motor recovery and functional independence for patients with neurological disorders. Neurorehabilitation with robotic devices has demonstrated its potential to provide intensive and repetitive training to promote motor recovery. These robotic devices can provide different types of assistance and can be controlled by different input modalities such as electromyography (EMG), electroencephalography (EEG), and kinematic signals. Among the different input modalities, EMG has been widely used, as it can provide real-time information about the patient’s muscle activation patterns and allow for a biocooperative control between the user and the robot. However, physiological acquisition systems are too expensive, and often bulky, to be used in clinical settings. Moreover, these systems typically lack processing capabilities, thereby impeding the development of real-time biocooperative control strategies and an efficient human-robot interaction. Additionally, biocooperative controls based on machine learning algorithms have not been implemented in real environments, mainly due to reliability issues or the need of a robot with a large number of degrees of freedom. In this context, the present Doctoral Thesis is focused on development of biocooperative control strategies, coupled with the design of low-cost embedded solutions for physiological data acquisition that contribute to provide a real use by individuals suffering from neuromotor impairments. The studies included in this compendium of publications are primarily concern with motor rehabilitation of the upper-limb. This is a significant area of focus, as upper-limb paresis is among the most frequently observed outcomes of stroke, having a profound effect on the quality of life and independence of stroke survivors. The contributions of this study are canalized in three different ways. First, affordable solutions have been created for the acquisition of physiological signals and implementation of biocooperative control in real-time embedded systems. An EMG recording system and a wearable multimodal physiological acquisition system have been designed to enhance accessibility and facilitate the use of upper-limb biocooperative control in the clinical settings. Secondly, a non-pattern recognition-based EMG-driven control has been developed for hand rehabilitation robot. The system operates on a real-time embedded platform and has demonstrated favorable performance in terms of both accuracy and latency. Additionally, we found that providing EMG-based visual feedback to subjects during EMG-driven bilateral therapies led to significant improvements in performance. These results suggest that the feedback enables subjects to increase their control over the movement of the robotic platform by assessing their muscle activation in real-time. Lastly, an EMG&IMU-based control using virtual reality-based therapy, along with an adaptive assistive control (AAN) using a wrist rehabilitation robot, have been proposed and employed to validate the performance of the embedded multimodal acquisition platform. The wearable system, which integrates multiple sensors, wireless communication, and a high-efficiency real-time microcontroller, is characterized by its high versability and configurability. It has been verified that its low-cost does not compromise the quality of the signals and it could promote the development of real-time biocooperative controls for a wide range of neuromotor rehabilitation applications. Overall, the findings of this Doctoral Thesis could pave the way for the development of more affordable and effective robotic devices for upper-limb neurorehabilitation and provide insights into the design and implementation of biocooperative controls for neurorehabilitation platforms.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
info:eu-repo/semantics/publishedVersion
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.35376/10324/62670
https://uvadoc.uva.es/handle/10324/62670
url https://doi.org/10.35376/10324/62670
https://uvadoc.uva.es/handle/10324/62670
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
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spelling Desarrollo de estrategias de control biocooperativo para plataformas robotizadas de rehabilitación neuromotoraCisnal De La Rica, AnaRoboticsRoboticsRobóticaNeurorehabilitationNeurorrehabilitaciónBiocooperativeBiocooperativoControlControl1207.02 Sistemas de ControlRehabilitation robotics has emerged as a promising solution to promote motor recovery and functional independence for patients with neurological disorders. Neurorehabilitation with robotic devices has demonstrated its potential to provide intensive and repetitive training to promote motor recovery. These robotic devices can provide different types of assistance and can be controlled by different input modalities such as electromyography (EMG), electroencephalography (EEG), and kinematic signals. Among the different input modalities, EMG has been widely used, as it can provide real-time information about the patient’s muscle activation patterns and allow for a biocooperative control between the user and the robot. However, physiological acquisition systems are too expensive, and often bulky, to be used in clinical settings. Moreover, these systems typically lack processing capabilities, thereby impeding the development of real-time biocooperative control strategies and an efficient human-robot interaction. Additionally, biocooperative controls based on machine learning algorithms have not been implemented in real environments, mainly due to reliability issues or the need of a robot with a large number of degrees of freedom. In this context, the present Doctoral Thesis is focused on development of biocooperative control strategies, coupled with the design of low-cost embedded solutions for physiological data acquisition that contribute to provide a real use by individuals suffering from neuromotor impairments. The studies included in this compendium of publications are primarily concern with motor rehabilitation of the upper-limb. This is a significant area of focus, as upper-limb paresis is among the most frequently observed outcomes of stroke, having a profound effect on the quality of life and independence of stroke survivors. The contributions of this study are canalized in three different ways. First, affordable solutions have been created for the acquisition of physiological signals and implementation of biocooperative control in real-time embedded systems. An EMG recording system and a wearable multimodal physiological acquisition system have been designed to enhance accessibility and facilitate the use of upper-limb biocooperative control in the clinical settings. Secondly, a non-pattern recognition-based EMG-driven control has been developed for hand rehabilitation robot. The system operates on a real-time embedded platform and has demonstrated favorable performance in terms of both accuracy and latency. Additionally, we found that providing EMG-based visual feedback to subjects during EMG-driven bilateral therapies led to significant improvements in performance. These results suggest that the feedback enables subjects to increase their control over the movement of the robotic platform by assessing their muscle activation in real-time. Lastly, an EMG&IMU-based control using virtual reality-based therapy, along with an adaptive assistive control (AAN) using a wrist rehabilitation robot, have been proposed and employed to validate the performance of the embedded multimodal acquisition platform. The wearable system, which integrates multiple sensors, wireless communication, and a high-efficiency real-time microcontroller, is characterized by its high versability and configurability. It has been verified that its low-cost does not compromise the quality of the signals and it could promote the development of real-time biocooperative controls for a wide range of neuromotor rehabilitation applications. Overall, the findings of this Doctoral Thesis could pave the way for the development of more affordable and effective robotic devices for upper-limb neurorehabilitation and provide insights into the design and implementation of biocooperative controls for neurorehabilitation platforms.El empleo de robots en el ámbito de la neurorrehabilitación surgió como una herramienta innovadora para facilitar el entrenamiento intensivo y repetitivo para promover la recuperación motora y la independencia funcional en pacientes con trastornos neurológicos. Los dispositivos robóticos en rehabilitación pueden proporcionar diferentes tipos de asistencia y pueden ser controlados por diferentes modalidades de entrada, como la electromiografía (EMG), la electroencefalografía (EEG) y la información cinemática. Entre los diferentes tipos de entrada, la EMG ha sido utilizada de forma extensiva, ya que proporciona información en tiempo real sobre los patrones de activación muscular y posibilita el control biocooperativo entre el usuario y el robot. Sin embargo, los sistemas de adquisición fisiológica son demasiado costosos y, a menudo, voluminosos para ser utilizados en entornos clínicos. Asimismo, estos sistemas suelen carecer de capacidades de procesamiento, lo que dificulta el desarrollo de estrategias de control en tiempo real y una interacción eficiente entre humano y robot. Además, los controles biocooperativos basados en algoritmos de aprendizaje automático no se han implementado en entornos reales, principalmente debido a problemas de confiabilidad o la necesidad de un robot con un gran número de grados de libertad. En este contexto, la presente Tesis Doctoral se centra en el desarrollo de estrategias de control biocooperativo y en el diseño de sistemas de bajo coste para la adquisición de señales fisiológicas, con el fin de proporcionar un uso real por parte de personas con discapacidades neuromotoras. Los estudios incluidos en este compendio de publicaciones están centrados en la rehabilitación motora del miembro superior, ya que es una de las consecuencias más frecuentes después de un accidente cerebrovascular, y tiene un gran impacto en la calidad de vida y la independencia de los pacientes. Las contribuciones de este estudio se canalizan en tres vías diferentes. En primer lugar, se han diseñado sistemas embebidos asequibles para la adquisición de señales fisiológicas y la implementación de controles biocooperativos en tiempo real. Se ha desarrollado un sistema de registro de EMG y sistema de adquisición multimodal para mejorar la accesibilidad de los sistemas biocooperativos de miembro superior en entornos clínicos. Por otra parte, se ha desarrollado un control impulsado por EMG no basado en reconocimiento de patrones para un robot de rehabilitación de mano. El control funciona en una plataforma embebida en tiempo real y ha demostrado un rendimiento favorable en términos de precisión y latencia. Además, se ha descubierto que proporcionar una retroalimentación visual basada en EMG durante terapias bilaterales impulsadas por EMG conduce a mejoras significativas en el rendimiento de los sujetos. Estos resultados sugieren que la retroalimentación permite aumentar el control de los sujetos sobre el movimiento de la plataforma robótica mediante la evaluación su activación muscular en tiempo real. Por último, se ha propuesto y desarrollado un control basado en EMG y en una unidad de medición inercial para la realización de terapias basadas en realidad virtual, y otro control con asistencia adaptativa utilizando un robot de rehabilitación de muñeca con el objetivo de validar el rendimiento del sistema embebido de adquisición multimodal. El sistema portátil, que integra múltiples sensores, comunicación inalámbrica y un microcontrolador de alta eficiencia en tiempo real, se caracteriza por su alta versatilidad y configurabilidad. Se ha verificado que su bajo costo no compromete la calidad de las señales y podría promover el desarrollo de controles biocooperativos en tiempo real para una amplia gama de aplicaciones de rehabilitación neuromotora. En general, los hallazgos de esta Tesis Doctoral podrían allanar el camino hacia el desarrollo sistemas biocooperativos para la neurorrehabilitación del miembro superior que sean más asequibles y efectivos.Escuela de DoctoradoDoctorado en Ingeniería IndustrialFraile Marinero, Juan CarlosPérez Turiel, JavierUniversidad de Valladolid. Escuela de Doctorado2023info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.35376/10324/62670https://uvadoc.uva.es/handle/10324/62670reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidInglésinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:uvadoc.uva.es:10324/626702026-06-13T12:44:47Z
score 15,301603

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