RESUMO
Noise pollution is a growing problem in urban areas, and it is important to study and evaluate its impact on human health and well-being. This work presents the design of a low-cost IoT model and implementation of two prototypes to collect noise level data in a specific area of the regional center of Chiriquí, at the Technological University of Panama that can be replicated to create a noise monitoring network. The prototypes were designed using Autodesk Fusion 360, and the data were stored in a MySQL database. Microsoft Excel and ArcGIS Pro were used to analyze the data, generate graphs, and display the information on maps. The results of the analysis can be used to develop strategies to reduce noise pollution and improve the quality of life in urban areas.
RESUMO
In this work, the authors present two eHealth platforms that are examples of how health systems are migrating from client-server architecture to the web-based and ubiquitous paradigm. These two platforms were modeled, designed, developed and implemented with positive results. First, using ambient-assisted living and ubiquitous computing, the authors enhance how palliative care is being provided to the elderly patients and patients with terminal illness, making the work of doctors, nurses and other health actors easier. Second, applying machine learning methods and a data-centered, ubiquitous, patient's results' repository, the authors intent to improve the Down's syndrome risk estimation process with more accurate predictions based on local woman patients' parameters. These two eHealth platforms can improve the quality of life, not only physically but also psychologically, of the patients and their families in the country of Panama.
