Browsing by Author "Grilo, Carlos"
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- 2ARTs: A Platform for Exercise Prescriptions in Cardiac Recovery PatientsPublication . Pereira, Andreia; Martinho, Ricardo; Pinto, Rui; Rijo, Rui; Grilo, CarlosDue to limited access, increasing costs and an ageing population, the global healthcare system faces significant coverage problems that call for innovative approaches. Health professionals are actively seeking alternative methods to provide care to an increasingly needy population, without increasing human effort and associated costs. eHealth platforms, which use technology to provide patient care, are emerging as transformative solutions for addressing these problems. This study is centered on the demand for a Decision Support System (DSS) in cardiology to enable doctors to prescribe individualized care inside Cardiac Rehabilitation Programmes (CRPs). The 2ARTs project’s main objective is to include a cardiac rehabilitation platform with a DSS within the hospital infrastructure. This DSS uses models to classify patients into different groups, delivering crucial information to assist with decisions regarding treatment. Regarding the DSS, Principal Component Analysis (PCA) emerged as a standout technique for dimensionality reduction, due to its interoperability with clustering algorithms and superior evaluation metrics. The most appropriate clustering technique was determined to be the K-means algorithm, which was supported by the experts analysis. In accordance with the goals of the 2ARTs project, this integration of PCA and K-means provides meaningful insights that improve reasoned decision-making.
- Cartesian genetic programming applied to pitch estimation of piano notesPublication . Inacio, Tiago; Miragaia, Rolando; Reis, Gustavo; Grilo, Carlos; Fernandez, FranciscoPitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. This paper presents a novel approach to the problem of Pitch Estimation, using Cartesian Genetic Programming (CGP). We take advantage of evolutionary algorithms, in particular CGP, to evolve mathematical functions that act as classifiers. These classifiers are used to identify piano notes' pitches in an audio signal. For a first approach, the obtained results are very promising: our error rate outperforms two of three state-of-the-art pitch estimators.
- CGP4Matlab - A Cartesian Genetic Programming MATLAB Toolbox for Audio and Image ProcessingPublication . Miragaia, Rolando; Jorge dos Reis, Gustavo Miguel; Fernandéz, Francisco; Inácio, Tiago; Grilo, CarlosThis paper presents and describes CGP4Matlab, a powerful toolbox that allows to run Cartesian Genetic Programming within MATLAB. This toolbox is particularly suited for signal processing and image processing problems. The implementation of CGP4Matlab, which can be freely downloaded, is described. Some encouraging results on the problem of pitch estimation of musical piano notes achieved using this toolbox are also presented. Pitch estimation of audio signals is a very hard problem with still no generic and robust solution found. Due to the highly flexibility of CGP4Matlab, we managed to apply a new cartesian genetic programming based approach to the problem of pitch estimation. The obtained results are comparable with the state of the art algorithms. © Springer International Publishing AG, part of Springer Nature 2018.
- Deep learning with realtime inference for human detection in search and rescuePublication . Llasag Rosero, Raúl; Grilo, Carlos; Silva, CatarinaHuman casualties in natural disasters have motivated tech- nological innovations in Search and Rescue (SAR) activities. Di cult ac- cess to places where res, tsunamis, earthquakes, or volcanoes eruptions occur has been delaying rescue activities. Thus, technological advances have gradually been nding their purpose in aiding to identify and nd the best locations to put available resources and e orts to improve rescue processes. In this scenario, the use of Unmanned Aerial Vehicles (UAV) and Computer Vision (CV) techniques can be extremely valuable for accelerating SAR activities. However, the computing capabilities of this type of aerial vehicles are scarce and time to make decisions is also rele- vant when determining the next steps. In this work, we compare di erent Deep Learning (DL) imaging detectors for human detection in SAR im- ages. A setup with drone-mounted cameras and mobile devices for drone control and image processing is put in place in Ecuador, where volcanic activity is frequent. The main focus is on the inference time in DL learn- ing approaches, given the dynamic environment where decisions must be fast. Results show that a slim version of the model YOLOv3, while using less computing resources and fewer parameters than the original model, still achieves comparable detection performance and is therefore more appropriate for SAR approaches with limited computing resources.
- Forum participation plugin for Moodle: Development and DiscussionPublication . Muñoz, Andrés; Delgado, Ramiro; Rubio, Enrique; Grilo, Carlos; Basto-Fernandes, VitorAt present, a large amount of software has been created to analyze social networks, such as libraries to access online social networking APIs, software to draw graphs and tools to use and analyze networks. In fact, and because of the use of Moodle as standard Learning Management System at the University of Las Palmas de Gran Canaria, in 2009 was born the idea of creating a plugin for Moodle capable of analyzing forums in which students participate and of identifying the major players within the student network. This work is about the present state of such plugin, which provides useful information to teachers so that, through the use of social network analysis, allows them to make decisions to improve and promote participatory education. Here, we show the application of the plugin to three case studies, in two different universities, which allowed to evaluate its usefulness and to compare the information according to the variables that influenced each case study.
- JavaScript Middleware for Mobile Agents Support on Desktop and Mobile PlatformsPublication . Silva, Carlos; Costa, Nuno; Grilo, Carlos; Veloz, JorgeThe evolution of technology in interconnection solutions such as Networks or the Internet, have allowed many communication architectures to be born and a varied interconnectivity. Here, we present a project that relies on the mobile agent computing paradigm. A middleware using the JavaScript language that allows the execution and ability to move mobile agents through the local network and Internet. This initiative arose as a way of dealing with problems raised by the considerable amount of existing Java based mobile agents middleware, which force the installation of the Java Virtual Machine in the devices, making complicated its execution in operating systems like macOS, iOS and others non-java friendly O.S. Our middleware works steadily in all operating systems, requiring only the installation of node.js. For mobile platforms the middleware is developed using React-native that allows it to run on mobile operating systems such as Android and iOS.
- mHealth Applications to Monitor Lifestyle Behaviors and Circadian Rhythm in Clinical Settings: Current Perspective and Future DirectionsPublication . Rosa, Iolanda; Lages, Marlene; Grilo, Carlos; Barros, Renata; Guarino, Maria P.Metabolic diseases are a global rising health burden, mainly due to the deleterious interaction of current lifestyles with the underlying biology of these diseases. Daily habits and behaviors, such as diet, sleep, and physical exercise impact the whole-body circadian system through the synchronization of the peripheral body clocks that contribute to metabolic homeostasis. The disruption of this system may promote the development of metabolic diseases, including obesity and diabetes, emphasizing the importance of assessing and monitoring variables that affect circadian rhythms. Advances in technology are generating innovative resources and tools for health care management and patient monitoring, particularly important for chronic conditions. The use of mobile health technologies, known as mHealth, is increasing and these approaches are contributing to aiding both patients and healthcare professionals in disease management and education. The mHealth solutions allow continuous monitoring of patients, sharing relevant information and data with physicians and other healthcare professionals and accessing education resources to support informed decisions. Thus, if properly used, these tools empower patients and help them to adopt healthier lifestyles. This article aims to give an overview of the influence of circadian rhythms disruption and lifestyle habits in the progression of metabolic diseases while also reviewing some of the mobile applications available to monitor lifestyle behaviors and individual chronobiology. Herein is also described the design and development of the NutriClock system, an mHealth solution developed by our team to monitor these variables.