The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Abstract

Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.

Classification of Human Hand Movements Using Surface EMG for Myoelectric Control

“…"EMG features extract structural characteristics from a single channel to describe the specific signal [5]." The features of muscle synergy are time-invariant, containing information from multiple EMG channels that can depict the underlying muscle coordination principles while performing various motions [5]. Root mean square (RMS) is selected as the EMG feature from the datasets.…”

“…Myoelectric control started to gain attention as a feature control mechanism in the 1940s [5]. It has the potential to lead a revolution in human-machine interaction due to its ability to measure human motion intention [5].…”

“…It has the potential to lead a revolution in human-machine interaction due to its ability to measure human motion intention [5]. After more than 60 years of research, however, myoelectric control is still struggling with the translation from research to clinical and commercial applications, such as exoskeletons, robot teleoperation, human-computer interface, and prostheses [12,4,10].…”

Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg’s Control

“…However, during the dynamic motion such as jumping and running, the multichannel sEMG signals vary greatly with time which may result in high risk for overfitting models to training data and frequent retraining [21]. Moreover, for the application of exoskeletons, myoelectric prostheses, and rehabilitation gait, it is always desirable to generate selfadapted gait with limited experimental data.…”

Brain-Swarm Control Interfaces: The Transition from Controlling One Robot to a Swarm of Robots

“…The author has extensive research back-ground on BMIs using electromyography (EMG) signals from upper limb muscles [8][9][10][11][12][13][14][15][16][17][18][19], and neural recordings [20,21]. We recently proposed that instead of using the decoder-based technique for BMIs, human subjects can learn to map their neural activity into control actions for an artificial system [22][23][24][25][26][27][28]. More specifically we have shown that subjects can control artificial systems using muscular activation, without requiring a decoding function to map one to the other.…”