Privacy-Preserving Blockchain Technologies.

The main characteristics of blockchains, such as security and traceability, have enabled their use in many distinct scenarios, such as the rise of new cryptocurrencies and decentralized applications (dApps). However, part of the information exchanged in the typical blockchain is public, which can lead to privacy issues. To avoid or mitigate these issues, some blockchains are applying mechanisms to deal with data privacy. Trusted execution environments, the basis of confidential computing, and secure multi-party computation are two technologies that can be applied in that sense. In this paper, we analyze seven blockchain technologies that apply mechanisms to improve data privacy. We define seven technical questions related to common requirements for decentralized applications and, to answer each question, we review the available documentation and gather information from chat channels. We briefly present each blockchain technology and the answers to each technical question. Finally, we present a table summarizing the information and showing which technologies are more prominent.

Mobility-Aware Federated Learning Considering Multiple Networks.

Federated learning (FL) is a distributed training method for machine learning models (ML) that maintain data ownership on users. However, this distributed training approach can lead to variations in efficiency due to user behaviors or characteristics. For instance, mobility can hinder training by causing a client dropout when a device loses connection with other devices on the network. To address this issue, we propose a FL coordination algorithm, MoFeL, to ensure efficient training even in scenarios with mobility. Furthermore, MoFeL evaluates multiple networks with different central servers. To evaluate its effectiveness, we conducted simulation experiments using an image classification application that utilizes machine models trained by a convolutional neural network. The simulation results demonstrate that MoFeL outperforms traditional training coordination algorithms in FL, with 156.5% more training cycles, in scenarios with high mobility compared to an algorithm that does not consider mobility aspects.

Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach.

BACKGROUND: Controlling the COVID-19 outbreak in Brazil is a challenge due to the population's size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources. OBJECTIVE: The purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies. METHODS: Raw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models' performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances. RESULTS: Gender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model. CONCLUSIONS: The DT classification model can effectively (with a mean accuracy≥89.12%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing.

An Acoustic Sensing Gesture Recognition System Design Based on a Hidden Markov Model.

Many human activities are tactile. Recognizing how a person touches an object or a surface surrounding them is an active area of research and it has generated keen interest within the interactive surface community. In this paper, we compare two machine learning techniques, namely Artificial Neural Network (ANN) and Hidden Markov Models (HMM), as they are some of the most common techniques with low computational cost used to classify an acoustic-based input. We employ a small and low-cost hardware design composed of a microphone, a stethoscope, a conditioning circuit, and a microcontroller. Together with an appropriate surface, we integrated these components into a passive gesture recognition input system for experimental evaluation. To perform the evaluation, we acquire the signals using a small microphone and send it through the microcontroller to MATLAB's toolboxes to implement and evaluate the ANN and HMM models. We also present the hardware and software implementation and discuss the advantages and limitations of these techniques in gesture recognition while using a simple alphabet of three geometrical figures: circle, square, and triangle. The results validate the robustness of the HMM technique that achieved a success rate of 90%, with a shorter training time than the ANN.

Design and evaluation of a mobile application to assist the self-monitoring of the chronic kidney disease in developing countries.

BACKGROUND: The chronic kidney disease (CKD) is a worldwide critical problem, especially in developing countries. CKD patients usually begin their treatment in advanced stages, which requires dialysis and kidney transplantation, and consequently, affects mortality rates. This issue is faced by a mobile health (mHealth) application (app) that aims to assist the early diagnosis and self-monitoring of the disease progression. METHODS: A user-centered design (UCD) approach involving health professionals (nurse and nephrologists) and target users guided the development process of the app between 2012 and 2016. In-depth interviews and prototyping were conducted along with healthcare professionals throughout the requirements elicitation process. Elicited requirements were translated into a native mHealth app targeting the Android platform. Afterward, the Cohen's Kappa coefficient statistics was applied to evaluate the agreement between the app and three nephrologists who analyzed test results collected from 60 medical records. Finally, eight users tested the app and were interviewed about usability and user perceptions. RESULTS: A mHealth app was designed to assist the CKD early diagnosis and self-monitoring considering quality attributes such as safety, effectiveness, and usability. A global Kappa value of 0.7119 showed a substantial degree of agreement between the app and three nephrologists. Results of face-to-face interviews with target users indicated a good user satisfaction. However, the task of CKD self-monitoring proved difficult because most of the users did not fully understand the meaning of specific biomarkers (e.g., creatinine). CONCLUSION: The UCD approach provided mechanisms to develop the app based on the real needs of users. Even with no perfect Kappa degree of agreement, results are satisfactory because it aims to refer patients to nephrologists in early stages, where they may confirm the CKD diagnosis.

A Standard-Based and Context-Aware Architecture for Personal Healthcare Smart Gateways.

The rising availability of Personal Health Devices (PHDs) capable of Personal Network Area (PAN) communication and the desire of keeping a high quality of life are the ingredients of the Connected Health vision. In parallel, a growing number of personal and portable devices, like smartphones and tablet computers, are becoming capable of taking the role of health gateway, that is, a data collector for the sensor PHDs. However, as the number of PHDs increase, the number of other peripherals connected in PAN also increases. Therefore, PHDs are now competing for medium access with other devices, decreasing the Quality of Service (QoS) of health applications in the PAN. In this article we present a reference architecture to prioritize PHD connections based on their state and requirements, creating a healthcare Smart Gateway. Healthcare context information is extracted by observing the traffic through the gateway. A standard-based approach was used to identify health traffic based on ISO/IEEE 11073 family of standards. A reference implementation was developed showing the relevance of the problem and how the proposed architecture can assist in the prioritization. The reference Smart Gateway solution was integrated with a Connected Health System for the Internet of Things, validating its use in a real case scenario.

Arguing effectiveness of biomedical signal acquisition devices using colored Petri Nets models and assurance cases in GSN: an ECG case study.

Reported cases of adverse events and product recalls expose limitations of biomedical signal acquisition devices. Approximately, ninety percent of the 1.210 recalls reported by the US Food and Drug Administration (FDA) between 2006 and 2011 were of class 2 devices such as Electrocardiography (ECG) devices. We show in this paper how manufacturers of biomedical signal acquisition devices can argue effectiveness of these devices using Colored Petri Nets (CPN) models and assurance cases in Goal Structuring Notation (GSN) by means of an ECG case study. We illustrate how CPN models are used to generate effectiveness evidences in order to present them during certification. In this context, we use assurance cases in GSN to present evidences arguing effectiveness of the device. We were able to conclude based on the ECG case study that the use of CPN models of devices can decrease costs and development time once manufacturers reuse them during the development and certification process.

A Model-Based Approach to Support Validation of Medical Cyber-Physical Systems.

Medical Cyber-Physical Systems (MCPS) are context-aware, life-critical systems with patient safety as the main concern, demanding rigorous processes for validation to guarantee user requirement compliance and specification-oriented correctness. In this article, we propose a model-based approach for early validation of MCPS, focusing on promoting reusability and productivity. It enables system developers to build MCPS formal models based on a library of patient and medical device models, and simulate the MCPS to identify undesirable behaviors at design time. Our approach has been applied to three different clinical scenarios to evaluate its reusability potential for different contexts. We have also validated our approach through an empirical evaluation with developers to assess productivity and reusability. Finally, our models have been formally verified considering functional and safety requirements and model coverage.

A Baseline Patient Model to Support Testing of Medical Cyber-Physical Systems.

Medical Cyber-Physical Systems (MCPS) are currently a trending topic of research. The main challenges are related to the integration and interoperability of connected medical devices, patient safety, physiologic closed-loop control, and the verification and validation of these systems. In this paper, we focus on patient safety and MCPS validation. We present a formal patient model to be used in health care systems validation without jeopardizing the patient's health. To determine the basic patient conditions, our model considers the four main vital signs: heart rate, respiratory rate, blood pressure and body temperature. To generate the vital signs we used regression models based on statistical analysis of a clinical database. Our solution should be used as a starting point for a behavioral patient model and adapted to specific clinical scenarios. We present the modeling process of the baseline patient model and show its evaluation. The conception process may be used to build different patient models. The results show the feasibility of the proposed model as an alternative to the immediate need for clinical trials to test these medical systems.