A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction

Abstract

This paper presents a literature review on pattern recognition of electromyography (EMG) signals and its applications. The EMG technology is introduced and the most relevant aspects for the design of an EMG-based system are highlighted, including signal acquisition and filtering. EMG-based systems have been used with relative success to control upper-and lower-limb prostheses, electronic devices and machines, and for monitoring human behavior. Nevertheless, the existing systems are still inadequate and are often abandoned by their users, prompting for further research. Besides controlling prostheses, EMG technology is also beneficial for the development of machine learning-based devices that can capture the intention of able-bodied users by detecting their gestures, opening the way for new human-machine interaction (HMI) modalities. This paper also reviews the current feature extraction techniques, including signal processing and data dimensionality reduction. Novel classification methods and approaches for detecting non-trained gestures are discussed. Finally, current applications are reviewed, through the comparison of different EMG systems and discussion of their advantages and drawbacks.INDEX TERMS EMG, human-machine interaction, pattern classification, regression.

EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges

“…Common EMG electrodes include two main kinds of pole placement methods: monopolar electrode and bipolar electrode. Both placing methods measure the potential with reference to the electrode placed at the locations without EMG response (e.g., ankle or knee), while the Monopoles method directly measures the potential difference and bipolar method apply the differentiate amplification method [ 52 ]. Bipolar electrodes have a higher usage frequency because the common mode noise can be suppressed in real application but lack of setup flexibility when compared to monopoles.…”

A 3DCNN-LSTM Hybrid Framework for sEMG-Based Noises Recognition in Exercise

“…Additionally, three main types of noise sources contribute to the process of sEMG signal acquisition, those are the inherent noise of electronic instrument, the ambient noise from the electromagnetic radiation in the environment, and motion artifact. These noise lies mostly in the frequency range from 0-20 Hz [17], [18]. Meanwhile, due to the quasi-random nature of the firing rate of the muscular motor units, the frequency components between 0 and 20 Hz are mostly unstable contains unstable components [17].…”

A Temporally Smoothed MLP Regression Scheme for Continuous Knee/Ankle Angles Estimation by Using Multi-Channel sEMG

“…For the sEMG-based classifications, only the on-off control was realized from sEMG signals in early days. Gradually, more discrete movements were recognized both on intact and trans-radial amputated subjects [15]. On the other hand, with the development of rehabilitation/exoskeleton robot systems, it is becoming more and more necessary to predict continuous movements and forces of human beings [17].…”