Simultaneous estimation of multi-finger forces by surface electromyography and accelerometry signals

Mao, He; Peng, Fang; Li, Guanglin

doi:10.1016/j.bspc.2021.103005

Cited by 16 publications

(11 citation statements)

References 28 publications

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“…For example, the functionality of a purely EMG-based bioelectric prosthesis is limited by the number of independent EMG signals that can be acquired from the remaining extremity [ 6 ]. Nowadays, machine learning methods are widely used for extracting control information from a larger number of channels [ 28 , 29 ]. Such approaches are based on classification of signals where a set of informative EMG signal features corresponds to a set of movements being performed.…”

Section: Discussionmentioning

confidence: 99%

“…This requirement also applies to the analysis of combined movements [ 29 ]. In this case, the EMG-based classification only allows performing one movement at a time without independent speed and force control of both the individual and the combined movements [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

“…To make the prosthesis control more comfortable, the current trends of perfecting bionic control systems are focused on combining the EMG signal with one or more signals based on a different physical method [ 28 , 43 ]. This combination of signals allows for significant improvement of control accuracy and reliability, as well as enriching the device functionality in terms of proportional and combined control.…”

Section: Discussionmentioning

confidence: 99%

“…The control principle of most EMG-based commercially available systems is based on simple method of proportional control first proposed in the 20th century [ 1 , 25 ]. For better user experience, more movement types should be identified, so, a greater number of measurement channels used simultaneously is required [ 26 , 27 , 28 , 29 ]. High-density EMG methods are used, they allow obtaining depolarization signals caused by the activity of certain muscles [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

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A Way of Bionic Control Based on EI, EMG, and FMG Signals

Briko

Kapravchuk

Kobelev

et al. 2021

Sensors

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Creating highly functional prosthetic, orthotic, and rehabilitation devices is a socially relevant scientific and engineering task. Currently, certain constraints hamper the development of such devices. The primary constraint is the lack of an intuitive and reliable control interface working between the organism and the actuator. The critical point in developing these devices and systems is determining the type and parameters of movements based on control signals recorded on an extremity. In the study, we investigate the simultaneous acquisition of electric impedance (EI), electromyography (EMG), and force myography (FMG) signals during basic wrist movements: grasping, flexion/extension, and rotation. For investigation, a laboratory instrumentation and software test setup were made for registering signals and collecting data. The analysis of the acquired signals revealed that the EI signals in conjunction with the analysis of EMG and FMG signals could potentially be highly informative in anthropomorphic control systems. The study results confirm that the comprehensive real-time analysis of EI, EMG, and FMG signals potentially allows implementing the method of anthropomorphic and proportional control with an acceptable delay.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Way of Bionic Control Based on EI, EMG, and FMG Signals

Briko

Kapravchuk

Kobelev

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Prior research investigated the possibility of estimating hand/fingerlevel forces from forearm EMG [Baldacchino et al 2018;Bardizbanian et al 2020a,b;Becker et al 2018;Castellini and Koiva 2012;Castellini and Van Der Smagt 2009;Cho et al 2022;Fang et al 2019;Gailey et al 2017;Liu et al 2013;Mao et al 2021;Martínez et al 2020;Martinez et al 2020;Wu et al 2020. Despite exciting preliminary results, deploying them in practical VR applications is still in its infancy.…”

Section: Emg-based Human-computer Interfacementioning

confidence: 99%