A Study of Movement Classification of the Lower Limb Based on up to 4-EMG Channels

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

Teachers’ Perception in Selecting Virtual Learning Platforms: A Case of Mexican Higher Education during the COVID-19 Crisis

In this project, an analysis is made of the perception of teachers of Higher Education Institutions (HEI) regarding the use of Virtual Learning Platforms (VLP) in the transition from the Traditional Educational Model (face-to-face) to Emergency Remote Teaching (ERT). A statistical analysis of teachers’ views on the academic environment caused by the COVID-19 crisis is carried out for the change of educational scenarios from school to home through VLP, the support for teaching–learning knowledge of VLPs and the received training, and the main problems during the transition period. Through convenience sampling, data was collected for a statistical study using a developed instrument (Data collection was designed through the Google Forms application and distributed to public and private HEI teachers). The results of the study showed that more than 60% of respondents had experience using Moodle, Google Classroom, and Blackboard; 80% of teachers had training from their institution for the use of virtual platforms; and in 60% of cases, higher education institutions allowed them to choose the VLP. In addition, the main issues they faced were connectivity, student attitude, and student attendance at class sessions. Fisher’s test was conducted to determine the relationship in the variables analyzed by identifying that there are differences of teachers in perception depending on age

Teachers’ Perception in Selecting Virtual Learning Platforms: A Case of Mexican Higher Education during the COVID-19 Crisis

In this project, an analysis is made of the perception of teachers of Higher Education Institutions (HEI) regarding the use of Virtual Learning Platforms (VLP) in the transition from the Traditional Educational Model (face-to-face) to Emergency Remote Teaching (ERT). A statistical analysis of teachers’ views on the academic environment caused by the COVID-19 crisis is carried out for the change of educational scenarios from school to home through VLP, the support for teaching–learning knowledge of VLPs and the received training, and the main problems during the transition period. Through convenience sampling, data was collected for a statistical study using a developed instrument (Data collection was designed through the Google Forms application and distributed to public and private HEI teachers). The results of the study showed that more than 60% of respondents had experience using Moodle, Google Classroom, and Blackboard; 80% of teachers had training from their institution for the use of virtual platforms; and in 60% of cases, higher education institutions allowed them to choose the VLP. In addition, the main issues they faced were connectivity, student attitude, and student attendance at class sessions. Fisher’s test was conducted to determine the relationship in the variables analyzed by identifying that there are differences of teachers in perception depending on age

Teaching Challenges in COVID-19 Scenery: Teams Platform-Based Student Satisfaction Approach

At the onset of the crisis caused by COVID-19, the Mexican education system chose to join the global context and suspend face-to-face classes for all educational levels. For the continuity of educational processes, a transition from a traditional educational model (face-to-face) to emergency remote teaching (ERT) was made through virtual learning platforms and learning management system (LMS) schemes. Universidad del Valle de Mexico (UVM), in a collaboration agreement with Microsoft Co., chose to use Teams to continue its educational process. In this work, we intend to identify the factors that can be taken into account regarding the level of student satisfaction in the teaching–learning process in ERT using Teams, and validate the established educational strategy. Statistical analysis was carried out to analyze the academic environment for these scenario changes while considering knowledge assessment, and competencies achievement. A combined sampling method was applied with convenience and statistical analysis. The main results established significant percentages, where more than 60% of the students surveyed were manifested in the use of the teams and the organization of the class sessions by the teachers, and the activities developed. Using the Cronbach’s Alpha coefficient, the reliability of the data collection instruments was determined. The correlations of each of the survey questions were calculated to determine the relationship between themselves and the total answers, giving results similar to those obtained through data science tools. Taking advantage of the situation, data science tools were applied to compare the results with obtained values from RapidMiner software in the correlation of factors in of 0.440, 0.384, 0.246, 0.048 and 0.384