According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.

The Computer Science department at Tecnológico Nacional de México-CENIDET, Mexico, with the collaboration of the Psychological department of the University of Castilla-La Mancha (UCLM), Spain, is on a developing process for the creation of an immersive virtual environment through virtual reality (VR) for the e-learning educational area. Such environment, works through electroencephalographic lectures (EEG) from the students, acquired by a Brain-Computer Interface (BCI), to adapt the virtual content to the profile and needs of the student on real-time basis. This system can detect the accuracy of attention and concentration levels on mental states, for the optimum development of the activities requested on an e-learning platform; if the student is not on a suitable concentration level, the system is able to induce the student to the requested mental state. The present document shows the proposal of different recommended activities that induce the mentioned mental states and the EEG response of each one. As well, the definition of the ideal learning emotional state that will be included as a part of the future works. It is important to mention that such activities are based on psychological researches that are dedicated to measure the levels of attention, concentration and other executive functions.

Traditional evaluation of user experience is subjective by nature, for what is sought is to use data from physiological and behavioral sensors to interpret the relationship that the user’s cognitive states have with the elements of a graphical interface and interaction mechanisms. This study presents the systematic review that was developed to determine the cognitive states that are being investigated in the context of Quality of Experience (QoE)/User Experience (UX) evaluation, as well as the signals and characteristics obtained, machine learning models used, evaluation architectures proposed, and the results achieved. Twenty-nine papers published in 2014–2019 were selected from eight online sources of information, of which 24% were related to the classification of cognitive states, 17% described evaluation architectures, and 41% presented correlations between different signals, cognitive states, and QoE/UX metrics, among others. The amount of identified studies was low in comparison with cognitive state research in other contexts, such as driving or other critical activities; however, this provides a starting point to analyze and interpret states such as mental workload, confusion, and mental stress from various human signals and propose more robust QoE/UX evaluation architectures.

E-learning resources are pieces of knowledge used for achieving a learning objective. One of the problems in e-training in the workplace has to do with the lack of relevance of learning resources. Here, we define a learning resource as a resource that contains information about a task or refers to a task realized within the context of an organizational process. We describe a set of activities for the generation of learning resources of objective, content, activity, and assessment types, which correspond to the pedagogical elements in learning objects. The generation goes from a documented business process using natural language processing and the learning resources produced are packaged as web services for independence and reusability purposes. Each learning resource generated is independent since it may participate in several formative sequences on its own, and it is reusable since it may be used many times without any change.

Depression is a prevalent mental health disorder, and early detection is crucial for effective intervention. Recent advancements in eye-tracking technology and machine learning offer new opportunities for non-invasive diagnosis. This study aims to assess the performance of different machine learning algorithms in. predicting depression in a young sample using eye-tracking metrics. Eye-tracking data from 139 participants were recorded with an emotional induction paradigm in which each participant observed a set of positive and negative emotional stimuli. The data were analyzed to find differences between groups, where the most significant features were selected to train prediction models. The dataset was then split into training and testing sets using stratified sampling. Four algorithms support vector machines (SVM), random forest (RF), a multi-layer perceptron (MLP) neural network, and gradient boosting (GB) were trained with hyperparameter optimization and 5-fold cross-validation. The RF algorithm achieved the highest accuracy at 84%, followed by SVM, GB, and the MLP neural network. Performance metrics such as accuracy, recall, F1-score, precision recall area under the curve (PR-AUC), and Matthews Correlation Coefficient (MCC) were also used to evaluate the models. The findings suggest that eye-tracking metrics combined with machine learning algorithms can effectively identify depressive symptoms in the young, indicating their potential as non-invasive diagnostic tools in clinical settings.