Hand gesture recognition (HGR) systems using electromyography (EMG) bracelet-type sensors are currently largely used over other HGR technologies. However, bracelets are susceptible to electrode rotation, causing a decrease in HGR performance. In this work, HGR systems with an algorithm for orientation correction are proposed. The proposed orientation correction method is based on the computation of the maximum energy channel using a synchronization gesture. Then, the channels of the EMG are rearranged in a new sequence which starts with the maximum energy channel. This new sequence of channels is used for both training and testing. After the EMG channels are rearranged, this signal passes through the following stages: pre-processing, feature extraction, classification, and post-processing. We implemented user-specific and user-general HGR models based on a common architecture which is robust to rotations of the EMG bracelet. Four experiments were performed, taking into account two different metrics which are the classification and recognition accuracy for both models implemented in this work, where each model was evaluated with and without rotation of the bracelet. The classification accuracy measures how well a model predicted which gesture is contained somewhere in a given EMG, whereas recognition accuracy measures how well a model predicted when it occurred, how long it lasted, and which gesture is contained in a given EMG. The results of the experiments (without and with orientation correction) executed show an increase in performance from 44.5% to 81.2% for classification and from 43.3% to 81.3% for recognition in user-general models, while in user-specific models, the results show an increase in performance from 39.8% to 94.9% for classification and from 38.8% to 94.2% for recognition. The results obtained in this work evidence that the proposed method for orientation correction makes the performance of an HGR robust to rotations of the EMG bracelet.

The mobility restrictions imposed in different countries due to the pandemic of Sars-CoV2 has hugely impacted different areas in the world. In this work, impacts on the social areas of Ecuadorian university students such as education, economy, physical and mental health, and access to telecommunications are analyzed. For this work, in a snapshot between May to September 2020, 1841 students from 6 public and 5 private universities from Ecuador were surveyed through 47 questions, which were grouped into 7 mutually exclusive dimensions. The Partial Least Squares Structural Equation Model (PLS-SEM) was used to analyze the correlations between the responses of the questions and the relations between dimensions. Dimensional relations were used to analyze how students perceive online classes, teachers’ preparation, mood, and the impact on their learning process due to their decreasing family income. Among the most important results, we found that 63.78% of students want to return to on-site classes regardless of their conditions of Internet connection and their available learning tools (computers, tablets, or cellphones). The results also show that family income has influenced how students access the Internet, Internet connection, technological resources for online learning, and mood. Regarding the relations between variables, we found that older students and students from higher semesters think that online classes are better than face-to-face classes and want to continue in online education.