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Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control

Published on Jul 1, 2015in IEEE Transactions on Neural Systems and Rehabilitation Engineering3.478
· DOI :10.1109/TNSRE.2015.2401134
Janne M. Hahne9
Estimated H-index: 9
,
Sven Dähne19
Estimated H-index: 19
+ 2 AuthorsLucas C. Parra51
Estimated H-index: 51
(CCNY: City College of New York)
Abstract
Myoelectric control of a prosthetic hand with more than one degree of freedom (DoF) is challenging, and clinically available techniques require a sequential actuation of the DoFs. Simultaneous and proportional control of multiple DoFs is possible with regression-based approaches allowing for fluent and natural movements. Conventionally, the regressor is calibrated in an open-loop with training based on recorded data and the performance is evaluated subsequently. For individuals with amputation or congenital limb-deficiency who need to (re)learn how to generate suitable muscle contractions, this open-loop process may not be effective. We present a closed-loop real-time learning scheme in which both the user and the machine learn simultaneously to follow a common target. Experiments with ten able-bodied individuals show that this co-adaptive closed-loop learning strategy leads to significant performance improvements compared to a conventional open-loop training paradigm. Importantly, co-adaptive learning allowed two individuals with congenital deficiencies to perform simultaneous 2-D proportional control at levels comparable to the able-bodied individuals, despite having to a learn completely new and unfamiliar mapping from muscle activity to movement trajectories. To our knowledge, this is the first study which investigates man-machine co-adaptation for regression-based myoelectric control. The proposed training strategy has the potential to improve myographic prosthetic control in clinically relevant settings.
  • References (40)
  • Citations (27)
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References40
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#1Ali Ameri (UNB: University of New Brunswick)H-Index: 5
#2Ernest Nlandu Kamavuako (AAU: Aalborg University)H-Index: 14
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This study describes the first application of a support vector machine (SVM) based scheme for real-time simultaneous and proportional myoelectric control of multiple degrees of freedom (DOFs). Three DOFs including wrist flexion–extension, abduction–adduction and forearm pronation–supination were investigated with 10 able-bodied subjects and two individuals with transradial limb deficiency (LD). A Fitts' law test involving real-time target acquisition tasks was conducted to compare the usability ...
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#1Han-Jeong Hwang (Technical University of Berlin)H-Index: 16
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Objective. Recent studies have shown the possibility of simultaneous and proportional control of electrically powered upper-limb prostheses, but there has been little investigation on optimal channel selection. The objective of this study is to find a robust channel selection method and the channel subsets most suitable for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom (DoFs). Approach. Ten able-bodied subjects and one person with congenital upper-li...
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#1Ali Ameri (UNB: University of New Brunswick)H-Index: 5
#2Ernest Nlandu Kamavuako (AAU: Aalborg University)H-Index: 14
Last. Philip A. Parker (UNB: University of New Brunswick)H-Index: 30
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Abstract A training strategy for simultaneous and proportional myoelectric control of multiple degrees of freedom (DOFs) is proposed. Ten subjects participated in this work in which wrist flexion–extension, abduction–adduction, and pronation–supination were investigated. Subjects were prompted to elicit contractions corresponding and proportional to the excursion of a moving cursor on a computer screen. Artificial neural networks (ANNs) were used to map the electromyogram (EMG) signals obtained ...
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The prediction of simultaneous limb motions is a highly desirable feature for the control of artificial limbs. In this work, we investigated different classification strategies for individual and simultaneous movements based on pattern recognition of myoelectric signals. Our results suggest that any classifier can be potentially employed in the prediction of simultaneous movements if arranged in a distributed topology. On the other hand, classifiers inherently capable of simultaneous predictions...
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We assessed the ability of four transradial amputees to control a virtual prosthesis capable of nine classes of movement both before and after a two-week training period. Subjects attended eight one-on-one training sessions that focused on improving the consistency and distinguishability of their hand and wrist movements using visual biofeedback from a virtual prosthesis. The virtual environment facilitated the precise quantification of three prosthesis control measures. During a final evaluatio...
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We propose an approach for online simultaneous and proportional myoelectric control of two degrees-of-freedom (DoF) of the wrist, using surface electromyographic signals. The method is based on the nonnegative matrix factorization (NMF) of the wrist muscle activation to extract low-dimensional control signals translated by the user into kinematic variables. This procedure does not need a training set of signals for which the kinematics is known (labeled dataset) and is thus unsupervised (althoug...
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In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques i...
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Stable myoelectric control of hand prostheses remains an open problem. The only successful human–machine interface is surface electromyography, typically allowing control of a few degrees of freedom. Machine learning techniques may have the potential to remove these limitations, but their performance is thus far inadequate: myoelectric signals change over time under the influence of various factors, deteriorating control performance. It is therefore necessary, in the standard approach, to regula...
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Abstract One of the most common issue in surface electromyography (sEMG) based myocontrol is to set a recurrent feature which allows to ensure a reliable multi degree of freedom (MDoF) prosthetic drive, mainly due to non-stationary behavior of signal. According to studies, electrode placement and shifts, variation in muscle contraction effort and muscle fatigue are the most common disturbance sources in sEMG recording, which traduces into a cumbersome donning and doffing recalibration. Many rele...
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State-of-the-art high-end prostheses are electro-mechanically able to provide a great variety of movements. Nevertheless, in order to functionally replace a human limb, it is essential that each movement is properly controlled. This is the goal of prosthesis control, which has become a growing research field in the last decades, with the ultimate goal of reproducing biological limb control. Therefore, exploration and development of prosthesis control are crucial to improve many aspects of an amp...
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Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. Recently, studies on co-adaptation have highlighted the benefits of concurrent user learning and machine adaptation where systems can cope with deficiencies in the...
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Research on machine learning approaches for upper-limb prosthesis control has shown impressive progress. However, translating these results from the lab to patient’s everyday lives remains a challenge because advanced control schemes tend to break down under everyday disturbances, such as electrode shifts. Recently, it has been suggested to apply adaptive transfer learning to counteract electrode shifts using as little newly recorded training data as possible. In this paper, we present a novel, ...
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