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Support Vector Regression for Improved Real-Time, Simultaneous Myoelectric Control

Published on Nov 1, 2014in IEEE Transactions on Neural Systems and Rehabilitation Engineering3.478
· DOI :10.1109/TNSRE.2014.2323576
Ali Ameri5
Estimated H-index: 5
(UNB: University of New Brunswick),
Ernest Nlandu Kamavuako14
Estimated H-index: 14
(AAU: Aalborg University)
+ 2 AuthorsPhilip A. Parker30
Estimated H-index: 30
(UNB: University of New Brunswick)
Abstract
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 of the SVM-based control system to that of an artificial neural network (ANN) based method. Performance was assessed using the Fitts' law throughput value as well as additional metrics including completion rate, path efficiency and overshoot. The SVM-based approach outperformed the ANN-based system in every performance measure $(p for able-bodied subjects. The SVM outperformed the ANN in path efficiency and throughput with the first LD subject and in throughput with the second LD subject. The superior performance of the SVM-based system appears to be due to its higher estimation accuracy of all DOFs during inactive and low amplitude segments (these periods were frequent during real-time control). Another advantage of the SVM-based method was that it substantially reduced the processing time for both training and real time control.
  • References (48)
  • Citations (64)
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References48
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#1Silvia Muceli (GAU: University of Göttingen)H-Index: 17
#2Ning Jiang (GAU: University of Göttingen)H-Index: 29
Last. Dario Farina (GAU: University of Göttingen)H-Index: 76
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Previous research proposed the extraction of myoelectric control signals by linear factorization of multi-channel electromyogram (EMG) recordings from forearm muscles. This paper further analyses the theoretical basis for dimensionality reduction in high-density EMG signals from forearm muscles. Moreover, it shows that the factorization of muscular activation patterns in weights and activation signals by non-negative matrix factorization (NMF) is robust with respect to the channel configuration ...
79 CitationsSource
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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#1Janne M. HahneH-Index: 9
#2F. BiebmannH-Index: 1
Last. Lucas C. Parra (CCNY: City College of New York)H-Index: 51
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#1Ernest Nlandu Kamavuako (AAU: Aalborg University)H-Index: 14
#2Erik Scheme (UNB: University of New Brunswick)H-Index: 10
Last. Kevin Englehart (UNB: University of New Brunswick)H-Index: 39
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Abstract The four main functions that are available in current clinical prostheses (e.g. Otto Bock DMC Plus ® ) are power grasp, hand open, wrist pronation and wrist supination. Improving the control of these two DoFs is therefore of great clinical and commercial interest. This study investigates whether control performance can be improved by targeting wrist rotator muscles by means of intramuscular EMG. Nine able-bodied subjects were evaluated using offline metrics and during a real-time contro...
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#1Ali Ameri (UNB: University of New Brunswick)H-Index: 5
#2Erik Scheme (UNB: University of New Brunswick)H-Index: 20
Last. Philip A. Parker (UNB: University of New Brunswick)H-Index: 30
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In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DO...
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#1Erik Scheme (UNB: University of New Brunswick)H-Index: 20
#2Blair A. Lock (Rehabilitation Institute of Chicago)H-Index: 19
Last. Kevin Englehart (UNB: University of New Brunswick)H-Index: 39
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This paper describes two novel proportional control algorithms for use with pattern recognition-based myoelectric control. The systems were designed to provide automatic configuration of motion-specific gains and to normalize the control space to the user's usable dynamic range. Class-specific normalization parameters were calculated using data collected during classifier training and require no additional user action or configuration. The new control schemes were compared to the standard method...
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#1Ernest Nlandu Kamavuako (AAU: Aalborg University)H-Index: 14
#2Jakob Celander Rosenvang (Capgemini)H-Index: 3
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The pattern recognition-based myoelectric control scheme is in the process of being implemented in clinical settings, but it has been mainly tested on sequential and steady state data. This paper investigates the ability of pattern recognition to resolve movements that are simultaneous and dynamically changing and compares the use of surface and untargeted intramuscular EMG signals for this purpose. Ten able-bodied subjects participated in the study. Both EMG types were recorded concurrently fro...
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#1Erik Scheme (UNB: University of New Brunswick)H-Index: 20
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When controlling a powered upper limb prosthesis it is important not only to know how to move the device, but also when not to move. A novel approach to pattern recognition control, using a selective multiclass one-versus-one classification scheme has been shown to be capable of rejecting unintended motions. This method was shown to outperform other popular classification schemes when presented with muscle contractions that did not correspond to desired actions. In this work, a 3-D Fitts' Law te...
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Jul 1, 2013 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Lauren H. Smith (Rehabilitation Institute of Chicago)H-Index: 11
#2Levi J. Hargrove (Rehabilitation Institute of Chicago)H-Index: 33
The simultaneous control of multiple degrees of freedom (DOFs) is important for the intuitive, life-like control of artificial limbs. The objective of this study was to determine whether the use of intramuscular electromyogram (EMG) improved pattern classification of simultaneous wrist/hand movements compared to surface EMG. Two pattern classification methods were used in this analysis, and were trained to predict 1-DOF and 2-DOF movements involving wrist rotation, wrist flexion/extension, and h...
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#1Erik Scheme (UNB: University of New Brunswick)H-Index: 20
#2Kevin EnglehartH-Index: 39
The performance of pattern recognition based myoelectric control has seen significant interest in the research community for many years. Due to a recent surge in the development of dexterous prosthetic devices, determining the clinical viability of multifunction myoelectric control has become paramount. Several factors contribute to differences between offline classification accuracy and clinical usability, but the overriding theme is that the variability of the elicited patterns increases great...
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