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SEMG-based Posture Recognition of Elbow Flexion and Extension

机译：基于SEMG的肘部屈曲和延伸的姿态识别

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Surface electromyographic signal (sEMG) is used in some fields such as human machine interaction and measurement of human motor function, because it can reflect the activation of human muscle. Though the recognition of motion pattern of human limbs has been researched for many years, continuous recognition for human elbow motion without load is still difficult because of low signal noise ratio (SNR). In this paper, we proposed an improved weighted peaks method to process the filtered sEMG signals from the biceps muscle and adapted linear fitting method to obtain the elbow motion in sagittal plane. The experiments showed the proposed method can effectively process the sEMG signals and obtain the activation of biceps muscle. The experimental results show the similar data of elbow motion compared to the data derived from an inertia sensor.
机译：表面电拍摄信号（SEMG）用于一些领域，例如人机相互作用和人体运动功能的测量，因为它可以反映人体肌肉的激活。虽然已经研究了人肢运动模式的识别多年来，由于低信噪比（SNR），仍然对没有负载的人肘运动的连续识别仍然困难。在本文中，我们提出了一种改进的加权峰值方法来处理来自二头肌肌肉的过滤的SEMG信号，并适应线性配合方法以获得矢状平面的弯头运动。实验表明，所提出的方法可以有效地处理SEMG信号并获得二头肌肌肉的激活。实验结果显示与惯性传感器的数据相比的肘关节运动数据。

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