KinesiOS: um sistema de captura de movimentos para avaliação motora e neurofuncional de pacientes em reabilitação pós-acidente vascular cerebral.
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Data
2021-07-30
Autores
Scudeletti, Luiz Rogério
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Universidade Estadual Paulista (Unesp)
Resumo
O Acidente Vascular Cerebral, também conhecido como AVC, é uma das condições médicas que mais mata e incapacita pessoas no mundo, atingindo homens, mulheres e crianças de diversas faixas etárias. O processo de reabilitação pós-AVC, na maioria das vezes se torna longo e tedioso, fazendo com que os pacientes não realizem os exercícios com a atenção necessária ou desistam das sessões de tratamento, o que pode diminuir as chances de recuperação completa. Estudos têm sido apresentados nos últimos anos abordando o uso de sistemas para captura de movimentos na reabilitação pós-AVC, mostrando que essas ferramentas podem ser tão eficientes quanto os métodos mais tradicionais. Neste trabalho, apresentamos o KinesiOS, um sistema de captura de movimentos para avaliação motora e neurofuncional de pacientes em reabilitação. O sistema rastreia as articulações do corpo humano com base em suas respectivas coordenadas espaciais e, em seguida, usa os dados obtidos para construir um guia de movimentos na forma de um esqueleto virtual, enquanto mede e exibe em tempo real a amplitude dos movimentos de determinadas ações motoras que são realizadas pelo paciente. O rastreio das articulações é realizado pelo sensor Microsoft Kinect v2, para o processamento dos dados é utilizada a linguagem de programação C#, as visualizações são criadas com a tecnologia Windows Presentation Foundation (WPF) e os dados são salvos em uma estrutura em nuvem, que utiliza a base de dados MongoDB. Testes preliminares realizados com seis voluntários saudáveis mostram a eficácia do sistema no cálculo da amplitude dos movimentos, possibilitando a análise dos dados em tempo real e via teleatendimento. O KinesiOS é uma ferramenta alternativa, portátil e de baixo custo, frente aos sistemas tradicionais baseados em rastreio de articulações.
The stroke (also known as a Cerebrovascular Accident) is one of the medical conditions that most kills and incapacitates people in the world, affecting men, women and children of many different age brackets. The post-stroke rehabilitation process, most of the times, becomes long and tedious, causing patients not to perform the exercises with the necessary attention or to give up on treatment sessions, which can decrease to the chances of complete recovery. Studies have been presented in recent years addressing the use of systems for motion capture in post stroke rehabilitation, showing that these tools could be just as efficient as the more traditional methods. In this study, we shall present KinesiOS, a system for recognition of movements for the motor and neurofunctional assessment of patients who are undergoing rehabilitation. The system tracks the joints in the human body based on their respective spatial coordinates, and then using the obtained data to construct a guide to movements in the form of a virtual skeleton, while measuring the amplitude of the movements (also known as a Range of Motion) within a certain motor action and showing the results in real time. The tracking of the joints is carried out using a Microsoft Kinect sensor v2, while data processing, we used the C# programming language. We created the visualizations using the Windows Presentation Foundation technology, and the data was saved in a cloud structure using the MongoDB database. Preliminary tests performed on six healthy volunteers showed the efficiency of the system for the calculation of amplitude of movements, enabling data analysis in real time and through telemonitoring. KinesiOS is an alternative tool, portable and low-cost, compared with the traditional systems based on tracking of joints.
The stroke (also known as a Cerebrovascular Accident) is one of the medical conditions that most kills and incapacitates people in the world, affecting men, women and children of many different age brackets. The post-stroke rehabilitation process, most of the times, becomes long and tedious, causing patients not to perform the exercises with the necessary attention or to give up on treatment sessions, which can decrease to the chances of complete recovery. Studies have been presented in recent years addressing the use of systems for motion capture in post stroke rehabilitation, showing that these tools could be just as efficient as the more traditional methods. In this study, we shall present KinesiOS, a system for recognition of movements for the motor and neurofunctional assessment of patients who are undergoing rehabilitation. The system tracks the joints in the human body based on their respective spatial coordinates, and then using the obtained data to construct a guide to movements in the form of a virtual skeleton, while measuring the amplitude of the movements (also known as a Range of Motion) within a certain motor action and showing the results in real time. The tracking of the joints is carried out using a Microsoft Kinect sensor v2, while data processing, we used the C# programming language. We created the visualizations using the Windows Presentation Foundation technology, and the data was saved in a cloud structure using the MongoDB database. Preliminary tests performed on six healthy volunteers showed the efficiency of the system for the calculation of amplitude of movements, enabling data analysis in real time and through telemonitoring. KinesiOS is an alternative tool, portable and low-cost, compared with the traditional systems based on tracking of joints.
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Palavras-chave
Reabilitação, Acidente vascular cerebral, Cinesiologia, Kinect v2, Rehabilitation, Stroke, Kinesiology