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

Ameri, Ali; Kamavuako, Ernest Nlandu; Scheme, Erik; Englehart, Kevin; Parker, P.A.

doi:10.1109/tnsre.2014.2323576

Cited by 154 publications

(122 citation statements)

References 48 publications

(43 reference statements)

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“…Most studies that investigate regression-based control used contraction profiles with combined DoFs in the training protocol [7], [8], [13]- [15]. While combined movements are usually not difficult to execute for able-bodied individuals, they are challenging to acquire by individuals with amputation or congenital limb deficiency as they do not have the intrinsic visual and proprioceptive feedback of the actual limb.…”

Section: Introductionmentioning

confidence: 99%

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

Hahne

Dähne

Hwang

et al. 2015

IEEE Trans. Neural Syst. Rehabil. Eng.

121

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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.

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Section: Introductionmentioning

confidence: 99%

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

Hahne

Dähne

Hwang

et al. 2015

IEEE Trans. Neural Syst. Rehabil. Eng.

121

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“…Third, studies have related forearm surface EMG to multiple DoF finger (Liu et al, 2013; Smith et al, 2009; Smith et al, 2008) and/or hand-wrist (Ameri et al, 2014a; Ameri et al, 2014b; Jiang et al, 2009; Jiang et al, 2012b; Muceli and Farina, 2012; Muceli et al, 2014; Nielsen et al, 2011) forces or kinematics, primarily in able-bodied subjects. Preliminary studies utilized high-density electrode arrays (32–64 + channels) (Liu, Brown, 2013; Muceli and Farina, 2012; Muceli, Jiang, 2014) or indwelling electrodes (Kamavuako et al, 2012; Kamavuako et al, 2014; Smith and Hargrove, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Ameri et al . (Ameri, Kamavuako, 2014a) related eight EMG signals to 1- and 2-DoF target displacements. Ten able-bodied subjects achieved average R 2 values from 82–90% and two limb-deficient subjects from 76–84%, depending on the DoF.…”

Section: Introductionmentioning

confidence: 99%

Two degrees of freedom quasi-static EMG-force at the wrist using a minimum number of electrodes

Clancy

Martinez-Luna

Wartenberg

et al. 2017

Journal of Electromyography and Kinesiology

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Surface electromyogram-controlled powered hand/wrist prostheses return partial upper-limb function to limb-absent persons. Typically, one degree of freedom (DoF) is controlled at a time, with mode switching between DoFs. Recent research has explored using large-channel EMG systems to provide simultaneous, independent and proportional (SIP) control of two joints—but such systems are not practical in current commercial prostheses. Thus, we investigated site selection of a minimum number of conventional EMG electrodes in an EMG-force task, targeting four sites for a two DoF controller. In a laboratory experiment with 10 able-bodied subjects and three limb-absent subjects, 16 electrodes were placed about the proximal forearm. Subjects produced 1-DoF and 2-DoF slowly force-varying contractions up to 30% maximum voluntary contraction (MVC). EMG standard deviation was related to forces via regularized regression. Backward stepwise selection was used to retain those progressively fewer electrodes that exhibited minimum error. For 1-DoF models using two retained electrodes (which mimics the current state of the art), subjects had average RMS errors of (depending on the DoF): 7.1–9.5 %MVC for able-bodied and 13.7–17.1 %MVC for limb-absent subjects. For 2-DoF models, subjects using four electrodes had errors on 1-DoF trials of 6.7–8.5 %MVC for able-bodied and 11.9–14.0 %MVC for limb-absent; and errors on 2-DoF trials of 9.9–11.2 %MVC for able-bodied and 15.8–16.7 %MVC for limb-absent subjects. For each model, retaining more electrodes did not statistically improve performance. The able-bodied results suggest that backward selection is a viable method for minimum error selection of as few as four electrode sites for these EMG-force tasks. Performance evaluation in a prosthesis control task is a necessary and logical next step for this site selection method.

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“…Others have successfully demonstrated that users can learn abstract mappings between EMG activity and prosthesis output (Radhakrishnan et al, 2008, Pistohl et al, 2013, Antuvan et al, 2014). Both linear (Hahne et al, 2014, Jiang et al, 2014b, Smith et al, 2015a) and nonlinear (Jiang et al, 2012, Hahne et al, 2014, Kamavuako et al, 2012, Ameri et al, 2014, Ngeo et al, 2014, Muceli and Farina, 2012) methods of mapping EMG activity to prosthesis movement have been evaluated, though a large emphasis of real-time evaluation has focused on linear methods and is commonly motivated by the motor control concept of muscle synergies (Jiang et al, 2009, d’Avella et al, 2006). EMG amplitude estimates (such as root-mean-square or mean absolute value (MAV)) are typically the primary signal feature used as inputs into these systems (Hahne et al, 2014, Jiang et al, 2014b, Smith et al, 2015a), as they positively correlate with contraction intensity (De Luca, 1997).…”

Section: Introductionmentioning

confidence: 99%

Use of probabilistic weights to enhance linear regression myoelectric control

2015

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Objective Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach Gaussian models were fit to EMG feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ Law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main Results Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p<0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

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

Cited by 154 publications

References 48 publications

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

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

Two degrees of freedom quasi-static EMG-force at the wrist using a minimum number of electrodes

Use of probabilistic weights to enhance linear regression myoelectric control

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