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Analysis and identification of the movements of a human arm using an electromyographic signal acquisition and processing system

机译:使用肌电信号采集和处理系统分析和识别手臂的运动

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Using MATLAB for the development of an EMG signal acquisition and processing system, to identify the different movements generated by a human arm such as flexion, extension, pronation and supination. Previously, information of the aforementioned movements is obtained to determine the best method of processing these signals generated by the movements. We proceed to program in Matlab to acquire and process these signals in real time. The acquisition system is made up of an EMG sensor implemented with an accelerometer sensor that helps determine the position and the set is connected to an Arduino Promicro that is configured as an interface for data acquisition with Matlab. These signals that are processed by Matlab will be shown by a graph. The sample to validate the equipment obtained a population of 94 people, where 96% of success was evidenced, this plus the statistical analysis of chi-square to evaluate and validate the hypothesis. According to the responses obtained in real time it was necessary to modify the position of the EMG device in the test arm, power source and have enough information to avoid errors. La muestra para validar el equipo se obtiene una población de 94 personas, en donde se evidencio el 96% de éxito, esto más el análisis estadístico de chi-cuadrado para evaluar y validar la hipótesis. Según las respuestas obtenidas en tiempo real se vio necesario modificar la posición del dispositivo EMG en el brazo de pruebas, fuente de alimentación y tener la suficiente información para evitar errores.
机译:使用MATLAB开发EMG信号采集和处理系统,以识别人手臂产生的不同运动,例如屈曲,伸展,内旋和旋后。先前,获得前述运动的信息以确定处理由运动产生的这些信号的最佳方法。我们继续在Matlab中进行编程,以实时获取和处理这些信号。采集系统由带有加速度传感器的EMG传感器组成,该传感器可帮助确定位置,并且该装置连接到Arduino Promicro,后者配置为与Matlab进行数据采集的接口。 Matlab处理的这些信号将通过图形显示。用于验证设备的样本获得了94个人的证明,其中96%的成功被证明是正确的,再加上卡方的统计分析以评估和验证假设。根据实时获得的响应,有必要修改EMG设备在测试臂,电源中的位置,并具有足够的信息以避免错误。公平,公正和公正的行为获得了94%的法律效力,法律效力的证据得到了有效的评估。实际发生的事例发生变更时,应在EMG帐户上保存适当的信息,然后再在适当的情况下纠正错误。

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