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.
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Veer, K. Flexible Approach for Classifying EMG Signals for Rehabilitation Applications. Neurophysiology 52, 60–66 (2020). https://doi.org/10.1007/s11062-020-09851-8
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DOI: https://doi.org/10.1007/s11062-020-09851-8