This work presents the modelling, design, construction, and control of an exoskeleton for ankle joint flexion/extension rehabilitation. The dynamic model of the ankle flexion/extension is obtained through Euler-Lagrange formulation and is built in Simulink of MATLAB using the non-linear differential equation derived from the dynamic analysis. An angular displacement feedback PID controller, representing the human neuromusculoskeletal control, is implemented in the dynamic model to estimate the joint torque required during ankle movements. Simulations are carried out in the model for the ankle flexion/extension range of motion (ROM), and the results are used to select the most suitable actuators for the exoskeleton. The ankle rehabilitation exoskeleton is designed in SolidWorks CAD software, built through 3D printing in polylactic acid (PLA), powered by two on-board servomotors that deliver together a maximum continuous torque of 22 [kg cm], and controlled by an Arduino board that establishes Bluetooth communication with a mobile app developed in MIT App Inventor for programming the parameters of the rehabilitation therapies. The result of this work is a lightweight ankle exoskeleton, with a total mass of 0.85 [kg] including actuators (servomotors) and electronics (microcontroller and batteries), which can be used in telerehabilitation practices guaranteeing angular displacement tracking errors under 10%.
Hoy en día, las redes neuronales artificiales y el deep learning, son dos de las herramientas más poderosas del aprendizaje de máquina, que tienen por objetivo desarrollar sistemas que aprenden automáticamente, reconocen patrones, predicen comportamientos y generalizan información a partir de conjuntos de datos. Estasdos herramientas se han convertido en un potencial campo de investigación con aplicaciones a la ingeniería, no siendo la ingeniería biomédica la excepción. En este artículo se presenta una revisión actualizada de las principales aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica en las ramas de la ómica, la imagenología, las interfaces cerebro-máquina y hombre-máquina, y la gestión y administración de la salud pública; ramas que se extienden desde el estudio de procesos a nivel molecular, hasta procesos que involucran grandes poblaciones.Palabras clave:aprendizaje de máquina; inteligencia artificial; reconocimiento de patrones; ómica; bioinformática; biomedicina; imagenología; interfaces cerebro-máquina; interfaces hombre-máquina;salud pública.
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