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首页> 外文期刊>Journal of applied statistics >Adaptive threshold method for peak detection of surface electromyography signal from around shoulder muscles
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Adaptive threshold method for peak detection of surface electromyography signal from around shoulder muscles

机译：自适应阈值法峰值检测肩膀周围肌肉表面肌电信号

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摘要

This paper illustrates the accurate identification of the surface electromyography signal obtained from the shoulder muscles (Teres, Trapezius and Pectoralis) of amputee subjects with three different arm motions (elevation, protraction and retraction). During the acquisition of the signal, a variety of variations (amplitude, frequency and noise) were introduced into the acquired signal which will misguide in the prediction of motion of the shoulder. Therefore, a novel approach has been aimed to adaptively adjust the threshold of Teager energy operator in order to filter the unwanted peaks in the pre-processing stage of the surface electromyography (SEMG) signal. Results show that the proposed approach is accurate and effective in the analysis of biomedical signal where peaks are important to detect without the knowledge of the shape of the waveform. As clinical research continues, these algorithms helps us to process SEMG signal and the identified signal would be used to design more accurate and efficient controllers for the upper-limb amputee.
机译：本文说明了从截肢者的肩膀肌肉（Teres，斜方肌和胸大肌）以三种不同的手臂动作（抬高，伸出和缩回）获得的表面肌电信号的准确识别。在获取信号的过程中，已将各种变化（幅度，频率和噪声）引入所获取的信号中，这会误导肩部运动的预测。因此，一种新颖的方法旨在自适应地调整Teager能量算子的阈值，以便在表面肌电图（SEMG）信号的预处理阶段中过滤掉不需要的峰值。结果表明，所提出的方法在分析生物医学信号时是准确而有效的，其中在不了解波形形状的情况下，检测峰值很重要。随着临床研究的继续，这些算法帮助我们处理SEMG信号，并且识别出的信号将用于为上肢截肢者设计更准确和有效的控制器。

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