Non-pathological mental fatigue is a recurring, but undesirable condition among people in the fields of office work, industry, and education. This type of mental fatigue can often lead to negative outcomes, such as performance reduction and cognitive impairment in education; loss of focus and burnout syndrome in office work; and accidents leading to injuries or death in the transportation and manufacturing industries. Reliable mental fatigue assessment tools are promising in the improvement of performance, mental health and safety of students and workers, and at the same time, in the reduction of risks, accidents and the associated economic loss (e.g., medical fees and equipment reparations). The analysis of biometric (brain, cardiac, skin conductance) signals has proven to be effective in discerning different stages of mental fatigue; however, many of the reported studies in the literature involve the use of long fatigue-inducing tests and subject-specific models in their methodologies. Recent trends in the modeling of mental fatigue suggest the usage of non subject-specific (general) classifiers and a time reduction of calibration procedures and experimental setups. In this study, the evaluation of a fast and short-calibration mental fatigue assessment tool based on biometric signals and inter-subject modeling, using multiple linear regression, is presented. The proposed tool does not require fatigue-inducing tests, which allows fast setup and implementation. Electroencephalography, photopletismography, electrodermal activity, and skin temperature from 17 subjects were recorded, using an OpenBCI helmet and an Empatica E4 wristband. Correlations to self-reported mental fatigue levels (using the fatigue assessment scale) were calculated to find the best mental fatigue predictors. Three-class mental fatigue models were evaluated, and the best model obtained an accuracy of 88% using three features, $\beta /\theta$ (C3), and the $\alpha /\theta$ (O2 and C3) ratios, from one minute of electroencephalography measurements. The results from this pilot study show the feasibility and potential of short-calibration procedures and inter-subject classifiers in mental fatigue modeling, and will contribute to the use of wearable devices for the development of tools oriented to the well-being of workers and students, and also in daily living activities.

Reliable and innovative methods for estimating forces are critical aspects of biomechanical sports research. Using them, athletes can improve their performance and technique and reduce the possibility of fractures and other injuries. For this purpose, throughout this project, we proceeded to research the use of video in biomechanics. To refine this method, we propose an RNN trained on a biomechanical dataset of regular runners that measures both kinematics and kinetics. The model will allow analyzing, extracting, and drawing conclusions about continuous variable predictions through the body. It marks different anatomical and reflective points (96 in total, 32 per dimension) that will allow the prediction of forces (N) in three dimensions ($F_x$, $F_y$, $F_z$), measured on a treadmill with a force plate at different velocities (2.5 m/s, 3.5 m/s, 4.5 m/s). In order to obtain the best model, a grid search of different parameters that combined various types of layers (Simple, GRU, LSTM), loss functions (MAE, MSE, MSLE), and sampling techniques (down-sampling, up-sampling) helped obtain the best performing model (LSTM, MSE, down-sampling) achieved an average coefficient of determination of 0.68, although when excluding $F_z$ it reached 0.92.

Promoting interest in Science, Technology, Engineering, and Mathematics (STEM) is a worldwide priority. Thus, accurate measures of professional interest, an aspect of personality according to vocational theories, are necessary. There is empirical evidence supporting personality prediction using biometric signals. Therefore, professional interest may be estimated similarly. The objective of this study was to generate a machine learning algorithm based on physiological data of children performing engineering-related activities that estimated their professional interest in engineering subfields. Thirteen children between 6 and 15 years old participated. Using eight electroencephalographic channels, we measured electrodermal activity, heart rate variability, facial gestures, and body temperature in four 2-hour sessions as the children performed engineering-related educational activities. Psychometric tests evaluated their interest in specific engineering subfields per the activities. We processed the generated data to design a machine learning algorithm, which resulted in 80% precision in detecting the change in interest. The results indicate that a pattern for change in engineering interest exists and can be measured.

Understanding the neural mechanisms underlying interest in Science, Technology, Engineering, and Mathematics (STEM) and learning is crucial for fostering creativity and problem-solving skills, key drivers of technological and educational growth. Traditional methods of assessing STEM interest are often limited by cultural and human biases, highlighting the need for more objective approaches. This study utilizes Electroencephalography (EEG) to identify neural markers linked to STEM interest and course-specific cognitive demands in young learners enrolled in a specialized private STEM program, including courses such as 3D Design, Programming, and Robotics. Specifically, Power Spectral Density (PSD) and Functional Connectivity (FC) were analyzed within theta, alpha, and beta frequency bands, which are associated with performance monitoring, creativity, and executive functioning. The findings reveal a significant negative correlation between STEM interest and brain activity in the frontal (F3, F4) and prefrontal regions (FP1, FP2) in the theta (r = -0.44, p = 0.2732; r = -0.77, p = 0.0268; r = -0.84, p = 0.0096; r = -0.62, p = 0.0990) and beta bands (r = 0.43, p = 0.2843; r = -0.56, p = 0.1524; r = -0.83, p = 0.0110; r = -0.75, p = 0.0328), indicating engagement in working memory and executive processing. Additionally, course-specific brain activity patterns reveal that Robotics is characterized by denser long-range FC networks, associated with problem-solving, while 3D Design exhibits more sparse yet efficient networks, indicative of creative ideation. A consistent beta band FC pattern between central and left-frontal areas reflects cognitive synchronicity and lateralization. These findings contribute to understanding the neurocognitive markers involved in STEM interest and learning, offering a framework for assessing and fostering engagement in STEM education through objective, neuroscience-based approaches.

This work presents a real-time biofeedback tool that employs wearables and the Internet of Things with educational applications to improve students' learning and retention. We aimed to create a web platform using the Internet of Things (IoT) and Machine Learning (ML) architecture to predict students' performance, analyze mental fatigue, and provide real-time quantitative biofeedback to identify the best learning modality. Thus, the main goal was to develop a system that allows students to learn and improve their projects. We integrated the analysis of real-time biometric signals, machine learning algorithms, and web services as we observed their behavior under different learning modalities, seeking to improve cognitive performance. For this, 23 volunteers filled out the ten-question Fatigue Assessment Scale questionnaire about mental fatigue, validated with the P300 waves acquired during auditory-oddball (AO) tests. Synchronized data acquisition was achieved using Enophones and an E4 wristband. To develop predictive models, we collected the biometric data and incorporated it into an ML algorithm to visualize students' performance in real time. The system can accommodate other wearable systems with new features in further experiments. Thus, we believe this current development has the potential to further revolutionize traditional teaching with this methodology and future enhancements.

Biometric systems provide the means to identify, authenticate and model individuals in a reliable way through the use of unique physiological and behavioural features. This chapter will explore different definitions, types of biometric systems, commercial and experimental technology, as well as the current and potential applications. The current sensing and processing methods devices and setups are summarized for physiological and behavioral biometry: from fingerprint and facial recognition to gait, speech and handwriting. Biometrics have been an important component in different applications such as security, military, and criminology; and new types of biometric systems are emerging as essential in healthcare, smart cities, and industrial business. Some seminal applications in education, neuromarketing and arts could lead all the efforts and the biometrics to become an everyday asset for the increasing of health and human performance in the near future of humanity.