Wearable and IoT technologies application for physical rehabilitation

This research consists in the development an IoT Physical Rehabilitation solution based on wearable devices, combining a set of smart gloves and smart headband for use in natural interactions with a set of VR therapeutic serious games developed on the Unity 3D gaming platform. The system permits to perform training sessions for hands and fingers motor rehabilitation. Data acquisition is performed by Arduino Nano Microcontroller computation platform with ADC connected to the analog measurement channels materialized by piezo-resistive force sensors and connected to an IMU module via I2C. Data communication is performed using the Bluetooth wireless communication protocol. The smart headband, designed to be used as a first- person-controller in game scenes, will be responsible for collecting the patient's head rotation value, this parameter will be used as the player's avatar head rotation value, approaching the user and the virtual environment in a semi-immersive way. The acquired data are stored and processed on a remote server, which will help the physiotherapist to evaluate the patients' performance around the different physical activities during a rehabilitation session, using a Mobile Application developed for the configuration of games and visualization of results. The use of serious games allows a patient with motor impairments to perform exercises in a highly interactive and non-intrusive way, based on different scenarios of Virtual Reality, contributing to increase the motivation during the rehabilitation process. The system allows to perform an unlimited number of training sessions, making possible to visualize historical values and compare the results of the different performed sessions, for objective evolution of rehabilitation outcome. Some metrics associated with upper limb exercises were also considered to characterize the patient’s movement during the session.Este trabalho de pesquisa consiste no desenvolvimento de uma solução de Reabilitação Física IoT baseada em dispositivos de vestuário, combinando um conjunto de luvas inteligentes e uma fita-de-cabeça inteligente para utilização em interações naturais com um conjunto de jogos terapêuticos sérios de Realidade Virtual desenvolvidos na plataforma de jogos Unity 3D. O sistema permite realizar sessões de treino para reabilitação motora de mãos e dedos. A aquisição de dados é realizada pela plataforma de computação Arduino utilizando um Microcontrolador Nano com ADC (Conversor Analógico-Digital) conectado aos canais de medição analógicos materializados por sensores de força piezo-resistivos e a um módulo IMU por I2C. A comunicação de dados é realizada usando o protocolo de comunicação sem fio Bluetooth. A fita-de-cabeça inteligente, projetada para ser usada como controlador de primeira pessoa nos cenários de jogo, será responsável por coletar o valor de rotação da cabeça do paciente, esse parâmetro será usado como valor de rotação da cabeça do avatar do jogador, aproximando o utilizador e o ambiente virtual de forma semi-imersiva. Os dados adquiridos são armazenados e processados num servidor remoto, o que ajudará o fisioterapeuta a avaliar o desempenho dos pacientes em diferentes atividades físicas durante uma sessão de reabilitação, utilizando uma Aplicação Móvel desenvolvido para configuração de jogos e visualização de resultados. A utilização de jogos sérios permite que um paciente com deficiências motoras realize exercícios de forma altamente interativa e não intrusiva, com base em diferentes cenários de Realidade Virtual, contribuindo para aumentar a motivação durante o processo de reabilitação. O sistema permite realizar um número ilimitado de sessões de treinamento, possibilitando visualizar valores históricos e comparar os resultados das diferentes sessões realizadas, para a evolução objetiva do resultado da reabilitação. Algumas métricas associadas aos exercícios dos membros superiores também foram consideradas para caracterizar o movimento do paciente durante a sessão

Erratum: Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning

Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning

Monitoring the condition of industrial equipment is fundamental to avoid failures and maximize uptime. The present work used supervised and unsupervised learning methods to create models for predicting the condition of an industrial machine. The main objective was to determine when the asset was either in its nominal operation or working outside this zone, thus being at risk of failure or sub-optimal operation. The results showed that it is possible to classify the machine state using artificial neural networks. K-means clustering and PCA methods showed that three states, chosen through the Elbow Method, cover almost all the variance of the data under study. Knowing the importance that the quality of the lubricants has in the functioning and classification of the state of machines, a lubricant classification algorithm was developed using Neural Networks. The lubricant classifier results were 98% accurate compared to human expert classifications. The main gap identified in the research is that the found classification works only carried out classifications of present, short-term, or mid-term failures. To close this gap, the work presented in this paper conducts a long-term classification

Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning

Monitoring the condition of industrial equipment is fundamental to avoid failures and maximize uptime. The present work used supervised and unsupervised learning methods to create models for predicting the condition of an industrial machine. The main objective was to determine when the asset was either in its nominal operation or working outside this zone, thus being at risk of failure or sub-optimal operation. The results showed that it is possible to classify the machine state using artificial neural networks. K-means clustering and PCA methods showed that three states, chosen through the Elbow Method, cover almost all the variance of the data under study. Knowing the importance that the quality of the lubricants has in the functioning and classification of the state of machines, a lubricant classification algorithm was developed using Neural Networks. The lubricant classifier results were 98% accurate compared to human expert classifications. The main gap identified in the research is that the found classification works only carried out classifications of present, short-term, or mid-term failures. To close this gap, the work presented in this paper conducts a long-term classification

Development of the HIV360 international core set of outcome measures for adults living with HIV: a consensus process

© 2021 British HIV AssociationObjectives: HIV outcomes centre primarily around clinical markers with limited focus on patient-reported outcomes. With a global trend towards capturing the outcomes that matter most to patients, there is agreement that standardizing the definition of value in HIV care is key to their incorporation. This study aims to address the lack of routine, standardized data in HIV care. Methods: An international working group (WG) of 37 experts and patients, and a steering group (SG) of 18 experts were convened from 14 countries. The project team (PT) identified outcomes by conducting a literature review, screening 1979 articles and reviewing the full texts of 547 of these articles. Semi-structured interviews and advisory groups were performed with the WG, SG and people living with HIV to add to the list of potentially relevant outcomes. The WG voted via a modified Delphi process - informed by six Zoom calls - to establish a core set of outcomes for use in clinical practice. Results: From 156 identified outcomes, consensus was reached to include three patient-reported outcomes, four clinician-reported measures and one administratively reported outcome; standardized measures were included. The WG also reached agreement to measure 22 risk-adjustment variables. This outcome set can be applied to any person living with HIV aged > 18 years. Conclusions: Adoption of the HIV360 outcome set will enable healthcare providers to record, compare and integrate standardized metrics across treatment sites to drive quality improvement in HIV care.info:eu-repo/semantics/publishedVersio

Development of the HIV360 international core set of outcome measures for adults living with HIV: A consensus process.

ObjectivesHIV outcomes centre primarily around clinical markers with limited focus on patient-reported outcomes. With a global trend towards capturing the outcomes that matter most to patients, there is agreement that standardizing the definition of value in HIV care is key to their incorporation. This study aims to address the lack of routine, standardized data in HIV care.MethodsAn international working group (WG) of 37 experts and patients, and a steering group (SG) of 18 experts were convened from 14 countries. The project team (PT) identified outcomes by conducting a literature review, screening 1979 articles and reviewing the full texts of 547 of these articles. Semi-structured interviews and advisory groups were performed with the WG, SG and people living with HIV to add to the list of potentially relevant outcomes. The WG voted via a modified Delphi process - informed by six Zoom calls - to establish a core set of outcomes for use in clinical practice.ResultsFrom 156 identified outcomes, consensus was reached to include three patient-reported outcomes, four clinician-reported measures and one administratively reported outcome; standardized measures were included. The WG also reached agreement to measure 22 risk-adjustment variables. This outcome set can be applied to any person living with HIV aged > 18 years.ConclusionsAdoption of the HIV360 outcome set will enable healthcare providers to record, compare and integrate standardized metrics across treatment sites to drive quality improvement in HIV care