Técnicas de controle em neuropróteses motoras baseadas no processamento de sinais biomédicos: uma investigação

Neuropróteses são dispositivos biomédicos desenvolvidos para restaurar ou compensar a funcionalidade de um órgão comprometido por disfunções neuronais causadas por diferentes patologias, como as lesões medulares, derrame ou doenças neurodegenerativas. Um projeto de neuroprótese é baseado na aquisição e processamento de um sinal biomédico que contenha a informação necessária para a ativação muscular, um dispositivo físico projetado para suportar cargas e amplitudes de um movimento e um sistema de controle que forneça ao paciente uma movimentação mais precisa dos membros. Há na literatura diversas neuropróteses documentadas que diferem entre si no tipo de sinal biomédico adquirido ou utilizado (EMG, EEG, parâmetros de marcha, estimulação funcional elétrica - FES); nas técnicas de processamento desses sinais (tempo-frequência); no formato e no biomaterial escolhido de acordo com a sua funcionalidade (membro superior ou inferior), assim como no sistema de controle utilizado (malha aberta, malha fechada ou na combinação de ambos). Este trabalho aborda estudos sobre técnicas de controle para neuropróteses. Eles foram agrupados em três categorias de neuropróteses baseando-se na técnica de controle usada. Entretanto, os estudos diferem entre si em muitos aspectos, tais como as técnicas de aquisição e processamento do sinal biológico (como EMG volitivo, EEG, FES e parâmetros de marcha) e os membros-alvo afetados pela patologia. Um estudo comparativo foi realizado entre as diferentes metodologias dos trabalhos selecionados destacando-se suas similaridades e diferenças em aplicações clínicas. Seus principais resultados são discutidos separadamente.Neuroprosthesis are biomedical devices designed to restore or compensate for the functionality of an organ compromised by neuronal dysfunctions caused by different pathologies, such as spinal cord injuries, stroke, or neurodegenerative diseases. A neuroprosthesis design is based on the acquisition and processing of a biomedical signal that contains the information needed for muscular activation, a physical device designed to support loads and ranges of motion, and a control system that provides the patient with a limb movement of greater precision. In the literature, there are many documented neuroprostheses that differ from each other in the type of biomedical signal acquired or utilized (EMG, EEG, gait parameters, functional electrical stimulation - FES); in the processing techniques of those signals (timefrequency); in the shape and biomaterial employed according to their functionality (inferior or superior limbs), as well as the control system utilized (open-loop, closedloop or the combination of both). This work addresses studies on control techniques for neuroprosthesis. They were grouped into three categories of neuroprosthesis based on the control technique used. These works differ on many other aspects though, such as the acquisition and processing techniques of biomedical signals (like volitional EMG, EEG, FES, and gait parameters), and target limbs affected by a movement disorder. A comparative study between those different development methodologies was performed, highlighting similarities and differences in clinical applications and discussing their results separately.Não recebi financiament

Forecast of the occupancy of standard and intensive care unit beds by COVID-19 inpatients

This paper proposes a methodology to forecast the number of hospital beds required by COVID-19 inpatients in mild and in critical conditions. To that end, a compartmental model is extended to include the number of critical inpatients, which require hospitalization in intensive care units (ICUs). The model parameters are tailored by using a data-driven approach and a computational methodology for numerical optimization. A multi-objective cost function is adopted, representing the match between the model output and the observed data for four variables, namely the total number of cases, demises, hospitalizations and ICU beds. Results for different regions of the Brazilian state of Sao Paulo are presented. The results show that the model represents well the training data and is able to predict the required health system resources.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)2020/14357-

COVID-19 trend analysis in mexican states and cities

This paper presents a trend analysis of the COVID-19 pandemics in Mexico. The studies were run in a subnational basis because they are more useful that way, providing important information about the pandemic to local authorities. Unlike classic approaches in the literature, the trend analysis presented here is not based on the variations in the number of infections along time, but rather on the predicted value of the final number of infections, which is updated every day employing new data. Results for four states and four cities, selected among the most populated in Mexico, are presented. The model was able to suitably fit the local data for the selected regions under evaluation. Moreover, the trend analysis enabled one to assess the accuracy of the forecasts.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)2020/14357-

Study of the COVID-19 pandemic trending behavior in Israeli cities

This paper studies the trending behavior of the COVID-19 dynamics in Israeli cities. The model employed is used to describe, for each city, the accumulated number of cases, the number of cases per day, and the predicted final number of cases. The innovative analysis adopted here is based on the daily evolution of the predicted final number of infections, estimated with data available until a given date. The results discussed here are illustrative for six cities in Israel, including Jerusalem and Tel Aviv. They show that the model employed fits well with the observed data and is able to suitably describe the COVID-19 dynamics in a country strongly impacted by the disease that holds one of the most successful vaccination programs in the world.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)2020/14357-12019/18294-

Information System for Epidemic Control: a computational solution addressing successful experiences and main challenges

Purpose: The SARS-CoV-2 pandemic has caused a major impact on worldwide public health and economics. The lessons learned from the successful attempts to contain the pandemic escalation revealed that the wise usage of contact tracing and information systems can widely help the containment work of any contagious disease. In this context, this paper investigates other researches on this domain, as well as the main issues related to the practical implementation of such systems, and specifies a technical solution. Methodology: The solution is based on the automatic identification of relevant contacts between infected or suspected cases with susceptible people; inference of contamination risk based on symptoms history, user navigation records, and contact information; real-time georeferenced information of population density of infected or suspect people; and automatic individual social distancing recommendation calculated through the individual contamination risk and the worsening of clinical condition risk. Findings: The solution was specified, prototyped, and evaluated by potential users and health authorities. The proposed solution has the potential of becoming a reference on how to coordinate the efforts of health authorities and the population on epidemic control. Originality: This paper proposed an original information system for epidemic control, which was applied for the SARS-CoV-2 pandemic and could be easily extended to other epidemics.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)2020/14357-