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- Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature ReviewPublication . Brites, Ivo; Martins da Silva, Lidia; Barbosa, Jorge; Rigo, S. J.; Correia, S. D.; LEITHARDT, VALDERI
- A Fog and Blockchain Software Architecture for a Global Scale Vaccination StrategyPublication . De Moura Costa, Humberto Jorge; Cristiano Andre Da Costa; Righi, Rodrigo; Antunes, Rodolfo S.; Juan Francisco De Paz Santana; LEITHARDT, VALDERINowadays, there are many fragmented records of patient’s health data in different locations like hospitals, clinics, and organizations all around the world. With the arrival of the COVID-19 pandemic, several governments and institutions struggled to have satisfactory, fast, and accurate decision-making in a wide, dispersed, and global environment. In the current literature, we found that the most common related challenges include delay (network latency), software scalability, health data privacy, and global patient identification. We propose to design, implement and evaluate a healthcare software architecture focused on a global vaccination strategy, considering healthcare privacy issues, latency mitigation, support of scalability, and the use of a global identification. We have designed and implemented a prototype of a healthcare software called Fog-Care, evaluating performance metrics like latency, throughput and send rate of a hypothetical scenario where a global integrated vaccination campaign is adopted in wide dispensed locations (Brazil, USA, and United Kingdom), with an approach based on blockchain, unique identity, and fog computing technologies. The evaluation results demonstrate that the minimum latency spends less than 1 second to run, and the average of this metric grows in a linear progression, showing that a decentralized infrastructure integrating blockchain, global unique identification, and fog computing are feasible to make a scalable solution for a global vaccination campaign within other hospitals, clinics, and research institutions around the world and its data-sharing issues of privacy, and identification.
- Derivative-Free Optimization with Proxy Models for Oil Production Platforms Sharing a Subsea Gas NetworkPublication . CARLOS, JÂNDER; Camponogara, Eduardo; Seman, Laio Oriel; Torreblanca González, José; LEITHARDT, VALDERIThe deployment of offshore platforms for the extraction of oil and gas from subsea reservoirs presents unique challenges, particularly when multiple platforms are connected by a subsea gas network. In the Santos basin, the aim is to maximize oil production while maintaining safe and sustainable levels of CO2 content and pressure in the gas stream. To address these challenges, a novel methodology has been proposed that uses boundary conditions to coordinate the use of shared resources among the platforms. This approach decouples the optimization of oil production in platforms from the coordination of shared resources, allowing for more efficient and effective operation of the offshore oilfield. In addition to this methodology, a fast and accurate proxy model has been developed for gas pipeline networks. This model allows for efficient optimization of the gas flow through the network, taking into account the physical and operational constraints of the system. In experiments, the use of the proposed proxy model in tandem with derivativefree optimization algorithms resulted in an average error of less than 5% in pressure calculations, and a processing time that was over up to 1000 times faster than the phenomenological simulator. These results demonstrate the effectiveness and efficiency of the proposed methodology in optimizing oil production in offshore platforms connected by a subsea gas network, while maintaining safe and sustainable levels of CO2 content and pressure in the gas stream.
- ID-Care: a Model for Sharing Wide Healthcare DataPublication . Humberto Jorge De Moura Costa; Cristiano Andre Da Costa; Antunes, Rodolfo S.; Righi, Rodrigo; Crocker, Paul; LEITHARDT, VALDERIAll over the world, there is a lot of patient health data in different locations such as hospitals, clinics, insurance companies, and other organizations. In this sense, global identification of the patient has emerged as an everyday healthcare challenge. Governments and institutions have to prioritize satisfactory, quick, and integrated decision-making in a wide, dispersed, and global environment because of unexpected challenges like pandemics or threats. In the current scientific literature, some of the existing challenges include support for a standard global unique identification that considers privacy issues, the combination of multiple technological biometry implementations, and personal documents. Thus, we propose a decentralized software model based on blockchain and smart contracts that includes privacy, global unique person identification supporting multiple combinations of documents, and biometric data using the Global Standards 1 - GS1 healthcare industry standard. Furthermore, we defined a methodology to evaluate a hypothetical use case of this model where an integrated and standard global health data sharing personal identification is crucial. For this, we implemented the proposed model in a global-wide continent location through cloud machines, fog computing, and blockchain considering the unique patient data identification and evaluate a use case scenario based on the top 5 most globally visited tourist destinations (France, Spain, the United States of America, China, and Italy), with an approach based on this model. The results show that using a model for a global id for healthcare can help reduce costs, time, and efforts, especially in the context of health threats, where agility and financial support must be prioritized.
- Analysis of Adaptive Algorithms Based on Least Mean Square Applied to Hand Tremor Suppression ControlPublication . Alves Araujo, Rafael Silfarney; Tironi, Jéssica Cristina; D. Parreira, Wemerson; Coelho Borges, Renata; Ruiz Juan, Francisco; LEITHARDT, VALDERIThe increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson’s disease (PD) or essential tremors (ETs). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3 Hz and 6 Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to ET, vary between 4 Hz and 12 Hz. We simulated and used these signals as reference signals in adaptive algorithms, filtered-x least mean square (Fx-LMS), filtered-x normalized least mean square (Fx-NLMS), and a hybrid Fx-LMS–NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS–LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case.
- WCIoT: A Smart Sensors Orchestration for Public Bathrooms using LoRaWANPublication . LEITHARDT, VALDERI
- Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic reviewPublication . Pinto, Rui João; Silva, Pedro Miguel; Duarte, Rui P.; Marinho, Francisco Alexandre; Pimenta, Luís; Gouveia, António Jorge; Gonçalves, N.J.A.P.; Coelho, Paulo; Zdravevski, Eftim; Lameski, Petre; LEITHARDT, VALDERI; Garcia, Nuno M.; Pires, Ivan MiguelThe prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.
- IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context HistoriesPublication . LEITHARDT, VALDERI