EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis

Abstract

BackgroundActive hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing. In this study, we present a novel concept to close the loop in myoelectric prostheses. In addition to conveying the grasping force (system output), we provided to the user the online information about the system input (EMG biofeedback).MethodsAs a proof-of-concept, the EMG biofeedback was transmitted in the current study using a visual interface (ideal condition). Ten able-bodied subjects and two amputees controlled a state-of-the-art myoelectric prosthesis in routine grasping and force steering tasks using EMG and force feedback (novel approach) and force feedback only (classic approach). The outcome measures were the variability of the generated forces and absolute deviation from the target levels in the routine grasping task, and the root mean square tracking error and the number of sudden drops in the force steering task.ResultsDuring the routine grasping, the novel method when used by able-bodied subjects decreased twofold the force dispersion as well as absolute deviations from the target force levels, and also resulted in a more accurate and stable tracking of the reference force profiles during the force steering. Furthermore, the force variability during routine grasping did not increase for the higher target forces with EMG biofeedback. The trend was similar in the two amputees.ConclusionsThe study demonstrated that the subjects, including the two experienced users of a myoelectric prosthesis, were able to exploit the online EMG biofeedback to observe and modulate the myoelectric signals, generating thereby more consistent commands. This allowed them to control the force predictively (routine grasping) and with a finer resolution (force steering). The future step will be to implement this promising and simple approach using an electrotactile interface. A prosthesis with a reliable response, following faithfully user intentions, would improve the utility during daily-life use and also facilitate the embodiment of the assistive system.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-015-0047-z) contains supplementary material, which is available to authorized users.

Explicit feedback and instruction do not change shoulder muscle activity reduction after shoulder fixation

“…However, in several previous studies EMG metrics have been used for biofeedback and in these studies participants are able to modulate EMG given instructions. This kind of EMG-based biofeedback has also been demonstrated to be useful for rehabilitation and prosthetic control (Dosen et al 2015;Giggins et al 2013;Kim 2017;Radhakrishnan et al 2008;Thompson and Wolpaw 2014;Woodford and Price 2007) . Lastly, although it is tempting to say that the learning we describe happens implicitly, the absence of an effect of explicit cues in this experiment does not mean that people are not engaged in different strategies (McDougle and Taylor 2019) .…”

Humans Can Integrate Augmented Reality Feedback in Their Sensorimotor Control of a Robotic Hand

“…Such feedback could include multiple variables to provide comprehensive information about the state of the prosthesis and/or highly dynamic signals such as feedback on EMG to allow for predictive control, as demonstrated in a recent study [36]. This is an advantage over other feedback paradigms particularly in the case of multiarticulated prostheses with independently controllable degrees of freedom [37] that could require more sophisticated feedback systems.…”

Joint Speed Discrimination and Augmentation For Prosthesis Feedback

“…Sensory feedback remains a research priority for prosthesis users 6 . Several feedback methods have been proposed over the past decades, including vibrotactile [7][8][9][10][11] , electrotactile 8 , skin stretch 7 , audio [12][13][14] and visual 15 modalities 16,17 . More complex feedback modalities like peripheral nerve stimulation 18 and vibration-induced illusory kinesthesia 19 have also been introduced to great effect.…”