Introduction: Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods: The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi's fractal dimension) combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM), K-nearest neighbors (KNN) and linear discriminant analysis (LDA) were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). Results: The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications.
Conclusion:The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.
A fisioterapia integra os cuidados do paciente submetido à cirurgia cardíaca. Porém são escassos estudos que relacionam esta ciência a pacientes que desenvolveram mediastinite pós-operatória. O objetivo do estudo foi relatar o acompanhamento fisioterapêutico em um caso clínico de mediastinite associada à Cirurgia de Revascularização Miocárdica realizada no Hospital Universitário Cassiano Antônio de Moraes (HUCAM). Desenvolveu-se um estudo de caso relatando a abordagem fisioterapêutica realizada ao paciente durante o período de internação hospitalar. O tratamento fisioterapêutico do paciente do estudo ocorreu duas vezes ao dia com os objetivos de restabelecer as funções pulmonares e a mobilidade para o retorno às atividades funcionais. As condutas aplicadas, junto às abordagens clínicas, visaram à melhora rápida e a alta hospitalar precoce. Conclui-se que a diversidade dessas situações e a existência de poucos estudos sobre a atuação fisioterapêutica nesses casos mostram uma lacuna de conhecimento nesta área da saúde e a possibilidade de explorar o assunto para ampliar a atuação deste profissional em futuros casos semelhantes.Palavras-chave: mediastinite, doenças cardiovasculares, Fisioterapia.
ResumenUno de los principales retos en el diseño de prótesis de mano es poder establecer un control intuitivo que reduzca el esfuerzo del usuario durante su entrenamiento. Este trabajo presenta un esquema para identificar tareas de motricidad fina de la mano, agrupadas en movimientos de los dedos individuales y gestos para el agarre de objetos el cual se ha validado con sujetos amputados. Se han comparado diferentes métodos de selección de características y clasificadores para el reconocimiento de patrones mioeléctricos, utilizando cuatro electrodos superficiales. Las características de las señales en el dominio del tiempo y la frecuencia se han combinado con métodos no lineales basados en análisis de fractales, mostrando una diferencia significativa en comparación con los métodos expuestos en la literatura para clasificar tareas de fuerza. Los resultados con amputados mostraron una exactitud de hasta 99,4% en los movimientos individuales de los dedos, superior a la obtenida con los gestos de agarre, de hasta 93,3%. El sistema ha obtenido una tasa de acierto promedio de 86,3% utilizando máquinas de soporte vectorial (SVM), seguido muy de cerca por K-vecinos más cercanos (KNN) con 83,4%. Sin embargo, KNN ha obtenido un mejor rendimiento global, debido a que es más rápido que SVM, lo que representa una ventaja para aplicaciones en tiempo real. El método aquí propuesto ofrece una mayor funcionalidad en el control de prótesis de mano, lo que mejoraría su aceptación por parte de los amputados.
Palabras Clave:Señales electromiográficas, prótesis de miembro superior, reconocimiento de patrones, tareas de destreza de la mano.
This research reports the identification of motor tasks in a human hand from weak myoelectric signals, aimed to control a prosthesis with individual finger flexion and wrist and grasps movements. The gestures were evaluated in two groups, independently. Four channel sEMG signals were captured on the forearm from able-body and amputees volunteers, taking into account low level contraction. Linear and non-linear parameters were extracted based on time and frequency domain and Detrended Fluctuation Analysis (DFA), to represent EMG patterns. The average classification accuracies were computed using Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) to evaluate the results. Confusion matrix from some experiments show the success rate identifying the gestures.Index Terms-sEMG, hand prostheses, myoelectric control, low level contraction, fractal analysis.
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