En la actualidad ha habido un incremento en la inseguridad y robos a viviendas, edificios y diversos lugares sociales, lo que ha llevado a la búsqueda e implementación de mecanismos que permitan proteger a las personas y a estos sitios. De esta manera, las casas con Sistemas de Seguridad Física (SSF) son menos afectados por estos hechos, y gracias al avance tecnológico y la aparición del Internet de las Cosas (IoT) dichos sistemas han mejorado bastante, llegando a suplir varias necesidades con funcionalidades novedosas, que son posibles por los nuevos dispositivos y tecnologías. En este sentido, es deseable resolver el cuestionamiento sobre ¿cuáles son los dispositivos y tecnologías del IoT más comúnmente utilizados en diferentes diseños de SSF? Así pues, el propósito de este artículo es presentar los resultados de la revisión literaria hecha, en la que se buscó identificar los elementos comunes utilizados en los SSF modernos basados en IoT, destacando las ventajas, desventajas y limitaciones de los sistemas implementados.

Comprehensive healthcare seeks to uphold the right to health by providing patient-centred care in both personal and work environments. However, the unequal distribution of healthcare services significantly restricts access in remote or underserved areas—a challenge that is particularly critical in mental health care within low-income countries. On average, there is only one psychiatrist for every 200,000 people, which severely limits early diagnosis and continuous monitoring in patients’ daily environments. In response to these challenges, this research explores the feasibility of implementing an information system that integrates cloud computing with an intelligent Facial Expression Recognition (FER) module to enable psychologists to remotely and periodically monitor patients’ emotional states. This approach enhances comprehensive clinical assessments, supporting early detection, ongoing management, and personalised treatment in mental health care. This applied research follows a descriptive and developmental approach, aiming to design, implement, and evaluate an intelligent cloud-based solution that enables remote monitoring of patients’ emotional states through Facial Expression Recognition (FER). The methodology integrates principles of user-centred design, software engineering best practices, and machine learning model development, ensuring a robust and scalable solution aligned with clinical and technological requirements. The development process followed the Software Development Life Cycle (SDLC) and included functional, performance, and integration testing. To assess overall system quality, we defined an evaluation framework based on ISO/IEC 25010 quality characteristics: functional suitability, performance efficiency, usability, and security. The intelligent FER model achieved strong validation results, with a loss of 0.1378 and an accuracy of 96%, as confirmed by the confusion matrix and associated performance metrics.

Blockchain technology is well known for the transparency and immutability of the data stored in it. These characteristics are essential in a document management system where security mechanisms are required to prevent information forgery. Therefore, several authors have chosen to take advantage of the benefits of Blockchain technology in their document management systems for academic, medical, and work-related documents, among others. However, its implementation must include elements that reinforce information security, since documents often contain sensitive information that requires special treatment. This article reviews the most recent articles on document management with Blockchain in order to extract the security elements implemented in their solutions and make a synthesis that can be used as a guide for anyone interested in developing a similar system.

Medical specialists who diagnose patients with cardiovascular arrhythmias are not promptly available in rural areas. The proposed information system provides access to upload the data to a cloud-based processing module in which different arrhythmias are classified, allowing the classification of various arrhythmias to solve the scarcity of physicians in remote areas through an automatic system that helps with the diagnosis. This information system has a controller module for the operation that manages data collection, processing, beautification and visualization tasks. We used the MIT-BIH Arrhythmia Database during the training and validation of the classification module. The module uses deep learning to identify five different types of heartbeats according to standards, reporting 98.9% accuracy. The information system will improve health coverage in the prediagnosis of cardiovascular diseases.