Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.

The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems—ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.

The use of wireless sensor networks (WSN) for monitoring variables in agricultural environments and natural forests has been increasing in recent years. However, the sizing of these systems is affected by the inaccuracy of the radio wave propagation models used, leading to possible increased costs and measurement errors. This systematic literature review (SLR) aims to identify propagation models widely used in WSN deployments in agricultural or naturally vegetated environments and their effectiveness in estimating signal losses. We also identified today’s wireless technologies most used in precision agriculture (PA) system implementations. In addition, the results of studies focused on the development of new propagation models for different environments are evaluated. Scientific and technical analysis is presented based on articles consulted in different specialized databases, which were selected according to different combinations of criteria. The results show that, in most of the application cases, vegetative models present high error values when estimating attenuation.

The field of healthcare holds significant global importance due to its profound impacts on both individual well-being and the broader healthcare system. It plays a pivotal role in the economic landscape, with far-reaching effects at the local, national, and global levels. Moreover, healthcare stands as a vital source of employment, supporting countless individuals across the world. It is a sector characterized by persistent challenges that have been met with innovation and technological advancements. In this literature review, our goal is to explore the key contributions in the healthcare domain, specifically in the diagnosis of diabetic and hypertensive retinopathy using advanced technologies such as Machine Learning and Artificial Intelligence (AI). The use of these technologies is instrumental in enhancing diagnostic accuracy and patient care. The wealth of research in this field is dispersed across various scholarly databases, presenting an opportunity for an extensive and focused investigation. By combining scientometric analysis with the metaphorical "tree of science," we can gain two valuable perspectives on this domain. The first perspective delves into scientometric statistics, shedding light on countries, authors, academic institutions, and research centers that are at the forefront of developing innovative solutions for diagnosing retinopathy using AI and Machine Learning. The second perspective employs an evolutionary analysis, exploring the origins of seminal research contributions and how they have evolved over time. This literature review underscores the ongoing relevance of leveraging Machine Learning and AI in healthcare, particularly in the diagnosis of retinopathy. Furthermore, the COVID-19 pandemic has accelerated the development of technologies that enable remote diagnosis and care, revolutionizing the healthcare landscape. As we navigate the intricate web of healthcare innovation, this literature review aims to provide a comprehensive understanding of the current state of research and its trajectory in the realm of diabetic and hypertensive retinopathy diagnosis through advanced technologies.

Tourism is a sector of high relevance worldwide, due to the multiple impacts it generates in local, regional, national, continental, and global economies, and it is a key generator of employment and provides sustenance to an innumerable number of people around the world. There have been many challenges at a global level to improve the user experience in a particular tourist place, where technology has played a highly relevant role in strengthening the conditions for tourists to achieve immersion in the culture, gastronomy, and recreation. The objective of this literature review is precisely to know and understand the key contributions that are currently being developed around the implementation of augmented reality as tourist technological support for user experiences. The literature on this topic is quite dispersed in specialized databases; therefore, it constitutes an opportunity to carry out a more detailed exploration of the topic. To address the different developments that have been carried out on tourism and augmented reality, an analysis was carried out based on the fusion of scientometric analysis and the metaphor of the Tree of Science, in which two relevant visions about the data were generated. The first focused on the different scientometric statistics regarding countries, authors, universities, or research or technological development centers that currently generate new applications based on augmented reality for tourism. The second focused on an evolutionary analysis based on the Tree of Science, analyzing the origins of the basic contributions of research and how it has evolved over time. This review indicates that the topic is currently valid and that it has been strengthened even more with the post-pandemic process, where many technological developments have been strengthened that allow people to enjoy tourist and cultural sites even without leaving home.