The electromyography (EMG) signal is particularly useful in monitoring muscle activity, and it can be acquired noninvasively on the skin surface. Thanks to these key characteristics, EMG-based human–machine interfaces (HMIs) for prosthetic myocontrol, as well as gesture recognition, are becoming widespread. A key challenge in this context is to design embedded systems to process EMG signals and generate motor commands with miniaturized, unobtrusive, and low-power devices, reliably and in real time, at a relatively low cost to provide continuous monitoring without causing stigma or discomfort. This article presents an in-depth review of the current status and future research challenges in systems and circuits for EMG acquisition and processing. We start by illustrating the sensor interfaces and acquisition systems required for signal analysis to provide efficient and effective ways of understanding the signal and its nature. We, then, focus on conventional state-of-the-art (SoA) EMG gesture recognition algorithms as well as novel architectures that tackle EMG processing challenges, i.e., hyperdimensional computing (HDC), blind source separation (BSS), and spiking neural networks (SNNs). Finally, we discuss open challenges, such as EMG variability, natural control, and efficient computation, to bring the myocontrol completely out of the laboratory, filling the gap between research prototypes and real-world applications.

Recently, the complexity analysis of brain activity has shown the possibility to provide additional information to discriminate between rest and motion in real-time. In this work, we propose a novel entropy-based machine learning method to classify between standing and walking conditions from the sole brain activity. The Shannon entropy has been used as a complexity measure of electroencephalography (EEG) signals and subject-specific features for classification have been selected by Common Spatial Patterns (CSP) filter. Exploiting these features with a linear classifier, we achieved > 85% of classification accuracy over a long period (≈ 25 min) of standing and treadmill walking on 11 healthy subjects. Moreover, we implemented the proposed approach to successfully discriminate in real-time between standing and over-ground walking on one healthy subject. We suggest that the reliable discrimination of rest against walking conditions achieved by the proposed method may be exploited to have more stable control of devices to restore locomotion, avoiding unpredictable and dangerous behaviors due to the delivery of undesired control commands.