Real time simultaneous and proportional control of multiple degrees of freedom from surface EMG: Preliminary results on subjects with limb deficiency

Rehbaum, Hubertus; Jiang, Ning; Paredes, Liliana; Amsuess, Sebastian; Gramlich, Bernhard; Farina, Dario

doi:10.1109/embc.2012.6346187

Cited by 37 publications

(38 citation statements)

References 4 publications

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“…Consequently, the regression approach provides greater flexibility for the user, leading towards a more intuitive prosthesis control paradigm. Examples for this control concept found in the literature are based on NonNegative Matrix Factorization [54,55,56 • ], Artificial Neural Networks [57][58][59][60][61][62], or Linear Regression [63], allowing two or more degrees of freedom to be activated simultaneously and proportionally. Notably, some of the limitations of pattern recognition systems have been addressed using regression approaches, such as electrode shift [64].…”

Section: Regression Controlmentioning

confidence: 99%

“…Formal performance assessment of control algorithms was validated using the target achievement control tests [38 • ], in which prosthetic users steered a virtual prosthesis into a predefined target position. This concept was streamlined using an abstract but more intuitive VRE paradigm of a simple arrow reaching circular targets [55].…”

Section: Virtual Rehabilitation Environmentsmentioning

confidence: 99%

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Prosthetic Myoelectric Control Strategies: A Clinical Perspective

et al. 2014

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Control algorithms for upper limb myoelectric prostheses have been in development since the mid-1940s. Despite advances in computing power and in the performance of these algorithms, clinically available prostheses are still based on the earliest control strategies. The aim of this review paper is to detail the development, advantages and disadvantages of prosthetic control systems and to highlight areas that are current barriers for the transition from laboratory to clinical practice. Current surgical strategies and future research directions to achieve multifunctional control will also be discussed. The findings from this review suggest that regression algorithms may offer an alternative feed-forward approach to direct and pattern recognition control, while virtual rehabilitation environments and tactile feedback could improve the overall prosthetic control.

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Section: Regression Controlmentioning

confidence: 99%

Section: Virtual Rehabilitation Environmentsmentioning

confidence: 99%

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

et al. 2014

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“…A solution to the supervision problem has been proposed in recent studies that used the synergy model to achieve continuous proportional and simultaneous control of multiple DOFs without the need for a time-demanding training phase [23]- [25]. In particular, Jiang and colleagues [24] achieved simultaneous continuous control of two DOFs with a semi-supervised approach that did not require a training session, but only a short initial calibration with information about which DOF was active.…”

Section: Introductionmentioning

confidence: 99%

Robustness and Reliability of Synergy-Based Myocontrol of a Multiple Degree of Freedom Robotic Arm

Lunardini

Casellato

d’Avella

et al. 2016

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-In this study, we test the feasibility of the synergybased approach for application in the realistic and clinically oriented framework of multi-degree of freedom (DOF) robotic control. We developed and tested online ten able-bodied subjects in a semi-supervised method to achieve simultaneous, continuous control of two DOFs of a robotic arm, using muscle synergies extracted from upper limb muscles while performing flexion-extension movements of the elbow and shoulder joints in the horizontal plane. To validate the efficacy of the synergy-based approach in extracting reliable control signals, compared to the simple muscle-pair method typically used in commercial applications, we evaluated the repeatability of the algorithm over days, the effect of the arm dynamics on the control performance, and the robustness of the control scheme to the presence of co-contraction between pairs of antagonist muscles. Results showed that, without the need for a daily calibration, all subjects were able to intuitively and easily control the synergy-based myoelectric interface in different scenarios, using both dynamic and isometric muscle contractions. The proposed control scheme was shown to be robust to co-contraction between antagonist muscles, providing better performance compared to the traditional muscle-pair approach. The current study is a first step toward user-friendly application of synergy-based myocontrol of assistive robotic devices.

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“…One of the main limitations of this multilayer perceptron approach is that it still requires the data from combined activation of multiple DOFs in the training set. Alternatively, non-negative matrix factorization has been used to estimate the joint force [5] and the joint angles [7,8]. This approach only requires data from single DOF activations for the controller calibration, but has inferior performance in comparison to multilayer perceptron network approach.…”

Section: Introductionmentioning

confidence: 99%

Myoelectric control of artificial limb inspired by quantum information processing

Siomau¹,

Jiang²

2015

Phys. Scr.

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Precise and elegant coordination of a prosthesis across many degrees of freedom represents a significant challenge to efficient rehabilitation of people with limb deficiency. Processing the electrical neural signals, collected from the surface of the remnant muscles of the stump, is a common way to initiate and control the different movements available to the artificial limb. Based on the assumption that there are distinguishable and repeatable signal patterns among different types of muscular activation, the problem of the prosthesis control reduces to one of pattern recognition. Widely accepted classical methods for pattern recognition, however, cannot provide simultaneous and proportional control of the artificial limb. Here we show that, in principle, quantum information processing of the neural signals allows us to overcome the above-mentioned difficulties suggesting a very simple scheme for myoelectric control of artificial limb with advanced functionalities.

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Real time simultaneous and proportional control of multiple degrees of freedom from surface EMG: Preliminary results on subjects with limb deficiency

Cited by 37 publications

References 4 publications

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

Robustness and Reliability of Synergy-Based Myocontrol of a Multiple Degree of Freedom Robotic Arm

Myoelectric control of artificial limb inspired by quantum information processing

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