EMG-driven exoskeleton control.
The need for mechanisms that assist human movements has been increasing due to the rising number of people that has some kind of movement disability. In this scenario, it is of great importance the development of control methods that assist the interface between a motor assistive device and its user...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Brasil |
| Institución: | Universidade de São Paulo (USP) |
| Repositorio: | Biblioteca Digital de Teses e Dissertações da USP |
| Idioma: | inglés |
| OAI Identifier: | oai:teses.usp.br:tde-14082019-150502 |
| Acceso en línea: | http://www.teses.usp.br/teses/disponiveis/3/3152/tde-14082019-150502/ |
| Access Level: | acceso abierto |
| Palabra clave: | Biomecânica Controle (Teoria de sistemas e controle) Electromyography Eletromiografia EMG Exoskeleton Identificação de sistemas Proportional control System identification |
| Sumario: | The need for mechanisms that assist human movements has been increasing due to the rising number of people that has some kind of movement disability. In this scenario, it is of great importance the development of control methods that assist the interface between a motor assistive device and its user. This work proposes a controller for an exoskeleton with one degree of freedom, using surface electromyography signals from the user as the input signal. An exoskeleton was adapted to serve as platform for the developed control method. To create an EMG-to-Angle model, a set of experiments were carried out with six subjects. The experiment consisted of a series of continuous and discrete elbow flexion and extension movements with different load levels. Using the experimental data, linear (ARIMAX) and non linear (Hammerstein-Wiener) system identification methods were evaluated to determine which is the best candidate for the estimation of the EMG-to-Angle model, based on its accuracy and ease of implementation. A new experiment wasconducted to develop a real-time controller, based on FIR model and tested in a real-timeapplication. Tests showed that the controller is capable of estimating the elbow joint angle with correlation above 70% and root-mean-square error below 25° when compared to the measured elbow joint angles. |
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