  Surface electromyography (sEMG) is a non-invasive method of measuring
neuromuscular potentials generated when the brain instructs the body to perform
both fine and coarse locomotion. This technique has seen extensive
investigation over the last two decades, with significant advances in both the
hardware and signal processing methods used to collect and analyze sEMG
signals. While early work focused mainly on medical applications, there has
been growing interest in utilizing sEMG as a sensing modality to enable
next-generation, high-bandwidth, and natural human-machine interfaces. In the
first part of this review, we briefly overview the human skeletomuscular
physiology that gives rise to sEMG signals followed by a review of developments
in sEMG acquisition hardware. Special attention is paid towards the fidelity of
these devices as well as form factor, as recent advances have pushed the limits
of user comfort and high-bandwidth acquisition. In the second half of the
article, we explore work quantifying the information content of natural human
gestures and then review the various signal processing and machine learning
methods developed to extract information in sEMG signals. Finally, we discuss
the future outlook in this field, highlighting the key gaps in current methods
to enable seamless natural interactions between humans and machines.
