Background: Alzheimer’s disease (AD) has paramount importance due to its rising prevalence, the impact on the patient and society, and the related healthcare costs. However, current diagnostic techniques are not designed for frequent mass screening, delaying therapeutic intervention and worsening prognoses. To be able to detect AD at an early stage, ideally at a pre-clinical stage, speech analysis emerges as a simple low-cost non-invasive procedure. Objectives: In this work it is our objective to do a systematic review about speech-based detection and classification of Alzheimer’s Disease with the purpose of identifying the most effective algorithms and best practices. Methods: A systematic literature search was performed from Jan 2015 up to May 2020 using ScienceDirect, PubMed and DBLP. Articles were screened by title, abstract and full text as needed. A manual complementary search among the references of the included papers was also performed. Inclusion criteria and search strategies were defined a priori. Results: We were able: to identify the main resources that can support the development of decision support systems for AD, to list speech features that are correlated with the linguistic and acoustic footprint of the disease, to recognize the data models that can provide robust results and to observe the performance indicators that were reported. Discussion: A computational system with the adequate elements combination, based on the identified best-practices, can point to a whole new diagnostic approach, leading to better insights about AD symptoms and its disease patterns, creating conditions to promote a longer life span as well as an improvement in patient quality of life. The clinically relevant results that were identified can be used to establish a reference system and help to define research guidelines for future developments.

One of the teacher’s first goals should be to inspire students to learn. Using project-based learning (PBL) to involve students in the learning process could be a useful and powerful tool to prepare the students for their professional future. As part of a degree course in Biomedical Engineering, students were asked to look at society and identify a possible biomedical-related failure or daily-life problem. From this, the students were challenged to work towards a solution, by preparing a project and creating a prototype or a minimum viable product. In this article we present the case study of a students’ team, whose project was candidate and winner of a national prize. This prize was related to health innovation. Despite the particularization of this case study case, the students considered the experience innovative, motivating, and challenging. They also underlined the added value of a project whose impact goes beyond the classroom. Therefore, this method of teaching and learning, based on projects, may have a special effect on the students and, therefore on the civil society. The PBL can help higher education institutions to have a more prominent social presence, as innovation drivers and as forces of intervention.

Gamification techniques have proven to be very effective in improving motivation and commitment, providing increased performance in both qualitative and quantitative terms. For this reason, it has been applied in more and more areas, with health and healthcare being no exception. The potential of this type of approach is enormous, and, on the one hand, it can motivate positive feelings; it can also foster deviant behaviors that fail to contribute to the individual and common good. This chapter aims to explore the relationship between the development of gamification systems and the ethical and moral aspects that are crucial elements when the target of the process becomes the human mind. The main questions and ethical dimensions that will allow us to constitute a reference framework for the development of gamification systems will be presented. Timely reflection and the inclusion of security mechanisms will allow us to develop better experiences for users, always combining improved motivation with the search for the good. Request access from your librarian to read this chapter's full text.

Artificial Intelligence (AI) has evolved rapidly since its inception in the 1950s, from simple rule-based systems to today's advanced deep learning models. AI has impacted society in many ways, ranging from revolutionizing the way we live, work, and interact with technology, to creating new job opportunities, improving decision-making and automating tasks, and solving complex problems in fields like healthcare, finance, and transportation. However, it has also raised concerns about job displacement, privacy and security, and ethical considerations. The evolution of AI is ongoing, and it is expected to continue to shape and transform society in new and profound ways. The impact of AI in education has also been substantial, offering new and innovative ways to personalize learning, enhance educational resources, and improve educational outcomes. In this chapter we will cover the most important aspects related with the teaching-learning process, from a physiological perspective to the different strategies.

The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations.

Background: Human emotions are subjective psychophysiological processes that play an important role in the daily interactions of human life. Emotions often do not manifest themselves in isolation; people can experience a mixture of them and may not express them in a visible or perceptible way; Methods: This study seeks to uncover EEG patterns linked to emotions, as well as to examine brain activity across emotional states and optimise machine learning techniques for accurate emotion classification. For these purposes, the DEAP dataset was used to comprehensively analyse electroencephalogram (EEG) data and understand how emotional patterns can be observed. Machine learning algorithms, such as SVM, MLP, and RF, were implemented to predict valence and arousal classifications for different combinations of frequency bands and brain regions; Results: The analysis reaffirms the value of EEG as a tool for objective emotion detection, demonstrating its potential in both clinical and technological contexts. By highlighting the benefits of using fewer electrodes, this study emphasises the feasibility of creating more accessible and user-friendly emotion recognition systems; Conclusions: Further improvements in feature extraction and model generalisation are necessary for clinical applications. This study highlights not only the potential of emotion classification to develop biomedical applications, but also to enhance human–machine interaction systems.