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Surface electromyography (sEMG) is a technology to assess muscle activation,which is an important component in applications related to diagnosis,treatment, progression assessment, and rehabilitation of specific individuals'conditions. Recently, sEMG potential has been shown, since it can be used in anon-invasive manner; nevertheless, it requires careful signal analysis tosupport health professionals reliably. This paper briefly described the basicconcepts involved in the sEMG, such as the physiology of the muscles, the dataacquisition, the signal processing techniques, and classification methods thatmay be used to identify disorders or signs of abnormalities according tomuscular patterns. Specifically, classification methods encompass digitalsignal processing techniques and machine learning with high potential in thefield. We hope that this work serves as an introduction to researchersinterested in this field.

Overview of processing techniques for surface electromyography signals