No abstract

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.

This paper presents a dynamic approach to the synthesis of a crank-rocker four-bar mechanism, that is obtained by an optimization problem and its solution using the Bioinspired algorithm called Differential Evolution (DE). The proposed dynamic approach states a mono-objective dynamic optimization problem (MODOP), in order to obtain a set of optimal parameters of the system. In this MODOP, the kinematic and dynamic models of the whole system are considered as well as a set of constraints including a dynamic constraint. The DE algorithm is adapted to solve the optimization problem by adding a suitable constraint-handling mechanism that is able to incorporate the kinematic and dynamic constraints of the system. A set of independent computational runs were carried out in order to validate the dynamic approach. An analysis from the mechanical and computational point of view is presented, based on the obtained results. From the analysis of the simulation and its results, it is shown that the solutions for the proposed algorithm lead to a more suitable design based on the dynamic approach.