Semiactive Knee Orthotic Using a MR Damper and a Smart Insole to Control the Damping Force Sensing the Plantar Pressure.

This work presents the development of semiactive knee orthosis prototype that focus to absorb the forces and impacts in this joint during the human gait. This prototype consists of three subsystems: the first is a wireless and portable system capable of measuring the ground reaction forces in the stance phase of the gait cycle, by means of an instrumented insole with force sensing resistors strategically placed on the sole of the foot, an electronic device allows processing and transmit this information via Bluetooth to the control system. The second is a semiactive actuator, which has inside a magnetorheological fluid, highlighting its ability to modify its damping force depending on the intensity of the magnetic field that circulates through the MR fluid. It is regulated by a Proportional Derivative (PD) controller system according to the values of plantar pressure measured by the insole. The third component is a mechanical structure manufactured by 3D printing, which adapts to the morphology of the human leg. This exoskeleton is designed to support the forces on the knee controlling the action of the magnetorheological actuator by ground reaction forces. The purpose of this assistance system is to reduce the forces applied to the knee during the gait cycle, providing support and stability to this joint. The obtained experimental results indicate that the device fulfills the function by reducing 12 % of the impact forces on the user's knee.

A Custom EOG-Based HMI Using Neural Network Modeling to Real-Time for the Trajectory Tracking of a Manipulator Robot.

Although different physiological signals, such as electrooculography (EOG) have been widely used in the control of assistance systems for people with disabilities, customizing the signal classification system remains a challenge. In most interfaces, the user must adapt to the classification parameters, although ideally the systems must adapt to the user parameters. Therefore, in this work the use of a multilayer neural network (MNN) to model the EOG signal as a mathematical function is presented, which is optimized using genetic algorithms, in order to obtain the maximum and minimum amplitude threshold of the EOG signal of each person to calibrate the designed interface. The problem of the variation of the voltage threshold of the physiological signals is addressed by means of an intelligent calibration performed every 3 min; if an assistance system is not calibrated, it loses functionality. Artificial intelligence techniques, such as machine learning and fuzzy logic are used for classification of the EOG signal, but they need calibration parameters that are obtained through databases generated through prior user training, depending on the effectiveness of the algorithm, the learning curve, and the response time of the system. In this work, by optimizing the parameters of the EOG signal, the classification is customized and the domain time of the system is reduced without the need for a database and the training time of the user is minimized, significantly reducing the time of the learning curve. The results are implemented in an HMI for the generation of points in a Cartesian space (X, Y, Z) in order to control a manipulator robot that follows a desired trajectory by means of the movement of the user's eyeball.