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Development of an interface for rehabilitation based on the EMG signal for the control of the ankle exoskeleton T-FLEX

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dc.contributor.advisorMúnera Ramirez, Marcela Cristina
dc.contributor.advisorCifuentes García, Carlos Andrés
dc.creatorCastellanos Guarnizo, Camila Andrea
dc.creator.degreeIngeniero Biomédicospa
dc.creator.degreetypeFull timespa
dc.date.accessioned2021-06-04T21:19:30Z
dc.date.available2021-06-04T21:19:30Z
dc.date.created2021-05-27
dc.descriptionEl accidente cerebrovascular es la segunda causa principal de muerte y la tercera de discapacidad, y el 75% de las personas que sufren un accidente cerebrovascular cada año experimentan limitaciones en la movilidad relacionadas con la marcha. Se han considerado estrategias que involucran dispositivos robóticos, como exoesqueletos y órtesis, para mejorar la rehabilitación del accidente cerebrovascular. Algunos de ellos han incluido la implementación de señales de Electromiografía (EMG) ya sea para análisis de activación muscular o detección de intención de movimiento. Este último ha estado involucrado en el proceso de activación de dispositivos robóticos para manejar la asistencia del dispositivo por la intención del sujeto de realizar un movimiento específico. Esto permitiría al sujeto involucrarse en su terapia. Por lo tanto, este proyecto introduce una interfaz EMG para el control del exoesqueleto del tobillo T-FLEX. Se revisaron algunos estudios donde se han incluido señales EMG en los procesos de control y terapia, y se analizaron algoritmos con diferentes métodos de cálculo de umbral. Teniendo en cuenta la información de esos estudios, se desarrolló un algoritmo basado en umbrales para la detección de la intención de movimiento. El algoritmo constaba de dos etapas principales, el cálculo del umbral y la detección de la intención del movimiento. La primera etapa consistió en el establecimiento del umbral a través de la extracción de características estadísticas (MEAN, desviación estándar (STD), varianza (VAR), MEAN + 3 * STD y Root Mean Square value (RMS)) de la señal de EMG. El segundo consistió en comparar la señal con el valor de referencia (umbral). Para probar el algoritmo se planificaron dos sesiones. En la primera sesión participaron diez sujetos sanos y su señal EMG se adquirió del músculo tibial anterior a través de un sensor muscular Myoware. Además, se colocó un sensor de Unidad de Medición Inercial (IMU) en la punta del pie de cada participante para adquirir la velocidad angular cuando se realizó la dorsiflexión del tobillo. Las señales de salida de ambos sensores se registraron y el procesamiento con el algoritmo se realizó off-line. La segunda sesión se realizó con el exoesqueleto de tobillo T-FLEX y un Juego Serio, implementando el algoritmo en tiempo real con una característica estadística seleccionada de la primera sesión como umbral. Se evaluó la detección del algoritmo EMG. También se evaluó el algoritmo que ya tenía T-FLEX para la detección de la intención de movimiento con el sensor IMU. Los resultados de la primera sesión mostraron que la característica de MEAN funcionó para el establecimiento del umbral con el sensor IMU, y para el sensor EMG fue (VAR), presentando un error menor al 10% en la cantidad de Falsos Positivos (FP) y Falsos Negativos (FN). Con esto, se llevó a cabo la segunda sesión, demostrando que había más precisión en el manejo del juego usando el sensor IMU que el sensor EMG. Con el sensor EMG la máxima precisión alcanzada fue del 89,7% y con el sensor IMU fue del 94,1%.spa
dc.description.abstractStroke is the second leading cause of death and third of disability, and 75% of individuals who sustain a stroke each year experience limitations in mobility-related to walking. Strategies involving robotic devices, such as exoskeletons and orthoses, have been considered to improve stroke rehabilitation. Some of them have included the implementation of Electromyography(EMG) signals either for muscle activation analysis or movement intention detection. The latter has been involved in the activation process of robotic devices to handle the device’s assistance by the subject’s intention to perform a specific movement. This would allow the subject to get involved in his/her therapy. Hence, this project introduces an EMG interface for the control of the ankle exoskeleton T-FLEX. Some studies where EMG signals have been included in control and therapy processes were reviewed, and algorithms with different threshold methods calculation were analyzed. Considering the information from those studies, a threshold-based algorithm for movement intention detection was developed. The algorithm consisted in two main stages, the threshold calculation and the movement intention detection. The first stage consisted on the threshold establishment through statistical features extraction (MEAN, standard deviation (STD), variance (VAR), MEAN + 3*STD and Root Mean Square value (RMS)) from the EMG signal. The second consisted of comparing the signal with the reference value (threshold).To test the algorithm, two sessions were planned. In the first session, ten healthy subjects participated and their EMG signal was acquired from the Tibialis Anterior muscle through a Myoware muscle sensor. Additionally, an Inertial Measurement Unit (IMU) sensor was placed on each participant’s foot tip to acquire the angular velocity when the ankle’s dorsiflexion was performed. The output signals from both sensors were recorded and the processing with the algorithm was done offline. The second session was carried out with the ankle exoskeleton T-FLEX and a Serious Game, implementing the algorithm in real-time with a statistical feature selected from the first session as the threshold. The detection from the EMG algorithm was evaluated. The algorithm that T-FLEX already had for the movement intention detection with the IMU sensor also was evaluated. The results from the first session showed that the MEAN feature worked for the threshold establishment with the IMU sensor, and for the EMG sensor was the (VAR), presenting an error of less than 10% in the amount of False Positive (FP) and False Negative (FN) values. With this, the second session was carried out, showing that there was more precision handling the game using the IMU sensor than the EMG sensor. With the EMG sensor the maximum precision achieved was 89,7% and with the IMU sensor was 94.1%.spa
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dc.identifier.doihttps://doi.org/10.48713/10336_31583
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/31583
dc.language.isoengspa
dc.publisherUniversidad del Rosariospa
dc.publisher.departmentEscuela de Medicina y Ciencias de la Saludspa
dc.publisher.programIngeniería Biomédicaspa
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
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dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
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dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subjectSensor EMGspa
dc.subjectSensor IMUspa
dc.subjectUmbralspa
dc.subjectIntención de movimientospa
dc.subjectExtracción de características estadísticasspa
dc.subject.ddcCiencias médicas, Medicinaspa
dc.subject.ddcIngeniería & operaciones afinesspa
dc.subject.keywordEMG sensorspa
dc.subject.keywordIMU sensorspa
dc.subject.keywordThresholdspa
dc.subject.keywordMovement intentionspa
dc.subject.keywordStatistical features extractionspa
dc.subject.lembDiseño en ingenieríaspa
dc.subject.lembElectromiografíaspa
dc.subject.lembRehabilitación médicaspa
dc.subject.lembTrastornos del movimientospa
dc.subject.lembBiosensoresspa
dc.titleDevelopment of an interface for rehabilitation based on the EMG signal for the control of the ankle exoskeleton T-FLEXspa
dc.title.TranslatedTitleDesarrollo de una interfaz para la rehabilitación basada en la señal de EMG para el control del exoesqueleto de tobillo T-FLEXspa
dc.typebachelorThesiseng
dc.type.documentTrabajo de gradospa
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersion
dc.type.spaTrabajo de gradospa
local.department.reportEscuela de Medicina y Ciencias de la Saludspa
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