The number and position of sEMG electrodes have been studied extensively due to the need to improve the accuracy of the classification they carry out of the intention of movement. Nevertheless, increasing the number of channels used for this classification often increases their processing time as well. This research work contributes with a comparison of the classification accuracy based on the different number of sEMG signal channels (one to four) placed in the right lower limb of healthy subjects. The analysis is performed using Mean Absolute Values, Zero Crossings, Waveform Length, and Slope Sign Changes; these characteristics comprise the feature vector. The algorithm used for the classification is the Support Vector Machine after applying a Principal Component Analysis to the features. The results show that it is possible to reach more than 90% of classification accuracy by using 4 or 3 channels. Moreover, the difference obtained with 500 and 1000 samples, with 2, 3 and 4 channels, is not higher than 5%, which means that increasing the number of channels does not guarantee 100% precision in the classification.

This paper presents a robot motion controller for an undergraduate laboratory study program. It is designed to help the students learn and to assess specific learning outcomes proposed by ABET by solving a real‐life problem. The main objective of this project is to enable the engineering students to learn some core concepts about embedded systems and motion controllers for robotics by applying them in practice. Also, the proposal shows how to introduce the students to a new tendency in the embedded system market, namely, an All Programmable System on a Chip (SoC). This methodology incorporates interdisciplinary knowledge, technical and professional skills required for pursuing a successful career. In the present study, we surveyed the observations and interests of students towards the learning process, and the results indicate that the inclusion of the robot prototype has a significant impact on providing students with new learning outcomes.

Electric vehicles (EVs) are an alternative to internal combustion engine (ICE) cars, as they can reduce the environmental impact of transportation. The bottleneck for EVs is the high-voltage battery pack, which utilizes most of the space and increases the weight of the vehicle. Currently, the main challenge for the electronics industry is the cell equalization of the battery pack. This paper gives an overview of the research works related to battery equalizer circuits (BECs) used in EV applications. Several simulations were carried out for the main BEC topologies with the same initial conditions. The results obtained were used to perform a quantitative analysis between these schemes. Moreover, this review highlights important issues, challenges, variables and parameters associated with the battery pack equalizers and provides recommendations for future investigations. We think that this work will lead to an increase in efforts on the development of an advanced BEC for EV applications.

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In this paper, an approach based on a liquid state machine (LSM) to compute the movement profiles to achieve a gait pattern subject to different variations in its trajectory is presented. At the same time, the position of the zero moment point (ZMP) to determine the stability of the six degrees-of-freedom (6DOF) bipedal robot in the sagittal plane during the gait cycle is calculated. The system is constructed as a supervised machine learning model. The time series of the oscillating foot trajectory obtained by direct kinematics with a multilayer perceptron neural network (MLP), to strengthen the kinematic model, is considered as input values for training. The target movement profiles are acquired of a human gait cycle analysis in three different scenarios: normal gait, climbing stairs, and descending stairs. In training, this model also gets the trajectories of the ZMP position during the gait cycle, as target time series. The LSM formed by spiking neurons, considered as third-generation neural networks, is compared in the accuracy of prediction, by the dynamic time warping (DTW) technique and correlation analysis, against the human gait analysis database. With this neuronal system, the joint positions to generate a trajectory of the oscillating foot and the ZMP position of the bipedal in the sagittal plane in different scenarios are obtained, proving the robustness of the LSM.