Generally, the system used for recording and analysis of surface electromyography (sEMG) signals consists of an acquisition card (data), a preamplifier, and a software code for signal acquisition and signal conditioning at different stages (e.g., utilization of wavelet transform) before feeding to statistical evaluation. In our study, basically, two independent muscle locations ( m.m. biceps and triceps of the human upper limb) were selected for the recording of data with multiple motion activities; analysis of the recorded data was carried out based on the extracted parameters. Further, a computational tool of analysis of variance (ANOVA) algorithm and wavelet decomposition db2 were implemented and consequently used for identifying the best mechanism for dual-channel muscle positions in the course of independent arm movements. The respective procedures are described; the results obtained are analyzed from the aspect of the possibility of their use for the control of a robot arm and upper limb prostheses.

中文翻译：

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用于康复应用的 EMG 信号分类的灵活方法

通常，用于表面肌电（sEMG）信号记录和分析的系统由采集卡（数据）、前置放大器和不同阶段的信号采集和信号调理（例如利用小波变换）的软件代码组成。用于统计评估。在我们的研究中，基本上选择了两个独立的肌肉位置（人类上肢的二头肌和三头肌）来记录具有多个运动活动的数据；基于提取的参数对记录的数据进行分析。此外，实施了方差分析 (ANOVA) 算法和小波分解 db2 的计算工具，并因此用于识别独立手臂运动过程中双通道肌肉位置的最佳机制。描述了各自的程序；所获得的结果从它们用于控制机器人手臂和上肢假肢的可能性方面进行了分析。