The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Levi J. Hargrove - One of the best experts on this subject based on the ideXlab platform.
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evaluation of linear regression simultaneous Myoelectric Control using intramuscular emg
IEEE Transactions on Biomedical Engineering, 2016Co-Authors: Lauren H. Smith, Todd A. Kuiken, Levi J. HargroveAbstract:Goal: The objective of this study was to evaluate the ability of linear regression models to decode patterns of muscle coactivation from intramuscular electromyogram (EMG) and provide simultaneous Myoelectric Control of a virtual 3-DOF wrist/hand system. Performance was compared to the simultaneous Control of conventional Myoelectric prosthesis methods using intramuscular EMG (parallel dual-site Control)—an approach that requires users to independently modulate individual muscles in the residual limb, which can be challenging for amputees. Methods: Linear regression Control was evaluated in eight able-bodied subjects during a virtual Fitts’ law task and was compared to performance of eight subjects using parallel dual-site Control. An offline analysis also evaluated how different types of training data affected prediction accuracy of linear regression Control. Results: The two Control systems demonstrated similar overall performance; however, the linear regression method demonstrated improved performance for targets requiring use of all three DOFs, whereas parallel dual-site Control demonstrated improved performance for targets that required use of only one DOF. Subjects using linear regression Control could more easily activate multiple DOFs simultaneously, but often experienced unintended movements when trying to isolate individual DOFs. Offline analyses also suggested that the method used to train linear regression systems may influence Controllability. Conclusion and Significance: Linear regression Myoelectric Control using intramuscular EMG provided an alternative to parallel dual-site Control for 3-DOF simultaneous Control at the wrist and hand. The two methods demonstrated different strengths in Controllability, highlighting the tradeoff between providing simultaneous Control and the ability to isolate individual DOFs when desired.
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Use of probabilistic weights to enhance linear regression Myoelectric Control
Journal of neural engineering, 2015Co-Authors: Lauren H. Smith, Todd A. Kuiken, Levi J. HargroveAbstract: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 electromyogram (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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Myoelectric Control System and Task-Specific Characteristics Affect Voluntary Use of Simultaneous Control
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2015Co-Authors: Lauren H. Smith, Todd A. Kuiken, Levi J. HargroveAbstract:Clinically available Myoelectric Control does not enable simultaneous proportional Control of prosthetic degrees of freedom. Multiple studies have proposed systems that provide simultaneous Control, though few have investigated whether subjects voluntarily use simultaneous Control or how they implement it. Additionally, few studies have explicitly evaluated the effect of providing proportional velocity Control. The objective of this study was to evaluate factors influencing when and how subjects use simultaneous Myoelectric Control, including the ability to proportionally Control the velocity and the required task precision. Five able-bodied subjects used simultaneous Myoelectric Control systems with and without proportional velocity Control in a virtual Fitts' Law task. Though subjects used simultaneous Control to a substantial degree when proportional velocity Control was present, they used very little simultaneous Control when using constant-velocity Control. Furthermore, use of simultaneous Control varied significantly with target distance and width, reflecting a strategy of using simultaneous Control for gross cursor positioning and sequential Control for fine corrective movements. These results provide insight into how users take advantage of simultaneous Control and highlight the need for real-time evaluation of simultaneous Control algorithms, as the potential benefit of providing simultaneous Control may be affected by other characteristics of the Myoelectric Control system.
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motion normalized proportional Control for improved pattern recognition based Myoelectric Control
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014Co-Authors: Erik Scheme, Levi J. Hargrove, Blair A Lock, Wendy Hill, Usha Kuruganti, Kevin EnglehartAbstract: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 of deriving proportional Control using a one degree of freedom Fitts' law test for each of the wrist flexion/extension, wrist pronation/supination and hand close/open degrees of freedom. Performance was evaluated using the Fitts' law throughput value as well as more descriptive metrics including path efficiency, overshoot, stopping distance and completion rate. The proposed normalization methods significantly outperformed the incumbent method in every performance category for able bodied subjects and nearly every category for amputee subjects. Furthermore, one proposed method significantly outperformed both other methods in throughput , yielding 21% and 40% improvement over the incumbent method for amputee and able bodied subjects, respectively. The proposed Control schemes represent a computationally simple method of fundamentally improving Myoelectric Control users' ability to elicit robust, and Controlled, proportional velocity commands.
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pattern recognition Control outperforms conventional Myoelectric Control in upper limb patients with targeted muscle reinnervation
International Conference of the IEEE Engineering in Medicine and Biology Society, 2013Co-Authors: Levi J. Hargrove, B. A. Lock, Ann M SimonAbstract:Pattern recognition Myoelectric Control shows great promise as an alternative to conventional amplitude based Control to Control multiple degree of freedom prosthetic limbs. Many studies have reported pattern recognition classification error performances of less than 10% during offline tests; however, it remains unclear how this translates to real-time Control performance. In this contribution, we compare the real-time Control performances between pattern recognition and direct Myoelectric Control (a popular form of conventional amplitude Control) for participants who had received targeted muscle reinnervation. The real-time performance was evaluated during three tasks; 1) a box and blocks task, 2) a clothespin relocation task, and 3) a block stacking task. Our results found that pattern recognition significantly outperformed direct Control for all three performance tasks. Furthermore, it was found that pattern recognition was configured much quicker. The classification error of the pattern recognition systems used by the patients was found to be 16% ±(1.6%) suggesting that systems with this error rate may still provide excellent Control. Finally, patients qualitatively preferred using pattern recognition Control and reported the resulting Control to be smoother and more consistent.
Ning Jiang - One of the best experts on this subject based on the ideXlab platform.
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Position Identification for Robust Myoelectric Control Against Electrode Shift
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2020Co-Authors: Xinjun Sheng, Xiangyang Zhu, Ning JiangAbstract:The vulnerability to the electrode shift was one of the key barriers to the wide application of pattern recognition-based (PR-based) Myoelectric Control systems outside the Controlled laboratory conditions. To overcome this challenge, a novel framework named position identification (PI) was proposed. In the PI framework, an anchor gesture performed by the user was first analyzed to identify the current electrode position from a pool of potential electrode shift positions. Next, the classifier calibrated by the data of the identified position would be selected for following Myoelectric Control tasks. The results of the amputee and able-bodied participants both demonstrated that the differential filter combined with majority voting improved the PI accuracy. With only one second contraction of the chosen anchor gesture (hand close), the subsequent PR-based Myoelectric Control performance was fully restored from eight different electrode shift scenarios, with 1 cm in either or both perpendicular and parallel directions. The classification accuracies with PI framework were not significant before and after the shift ( ${p} 0.001 ). The advantage of restoring performance fully in just one second made it a practical solution to improve the robustness of PR-based Myoelectric Control systems in a wide range of real-world applications.
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Spatial Information Enhances Myoelectric Control Performance With Only Two Channels
IEEE Transactions on Industrial Informatics, 2019Co-Authors: Xinjun Sheng, Xiangyang Zhu, Chaozhe Jiang, Ning JiangAbstract:Automatic gesture recognition (AGR) is investigated as an effortless human–machine interaction method, potentially applied in many industrial sectors. When using surface electromyogram (sEMG) for AGR, i.e., Myoelectric Control, a minimum of four EMG channels are required. However, in practical applications, fewer number of electrodes is always preferred, particularly for mobile and wearable applications. No published research focused on how to improve the performance of a Myoelectric system with only two sEMG channels. In this study, we presented a systematic investigation to fill this gap. Specifically, we demonstrated that through spatial filtering and electrode position optimization, the Myoelectric Control performance was significantly improved ( p ) and similar to that with four electrodes. Furthermore, we found a significant correlation between offline and online performance metrics in the two-channel system, indicating that offline performance was transferable to online performance, highly relevant for algorithm development for sEMG-based AGR applications.
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robust extraction of basis functions for simultaneous and proportional Myoelectric Control via sparse non negative matrix factorization
Journal of Neural Engineering, 2018Co-Authors: Chuang Lin, Ning Jiang, Binghui Wang, Dario FarinaAbstract:Objective. This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) Myoelectric Control method for active prostheses. Approach. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the Control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently. Main Results. In online testing based on target hitting, able-bodied subjects reached a greater throughput (TP) when using sparse NMF (SNMF) than with classic NMF or with linear regression (LR). Accordingly, the completion time (CT) was shorter for SNMF than NMF or LR. The same observations were made in two patients with unilateral limb deficiencies. Significance. The addition of sparseness constraints to NMF allows for a quasi-unsupervised approach to Myoelectric Control with superior results with respect to previous methods for the simultaneous and proportional Control of multi-DOF. The proposed factorization algorithm allows robust simultaneous and proportional Control, is superior to previous supervised algorithms, and, because of minimal supervision, paves the way to online adaptation in Myoelectric Control.
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A Biologically-Inspired Robust Control System for Myoelectric Control
Converging Clinical and Engineering Research on Neurorehabilitation II, 2016Co-Authors: Silvia Muceli, Ivan Vujaklija, Bernhard Graimann, Ning Jiang, Sebastian Amsuess, Oskar C. Aszmann, Dario FarinaAbstract:We review recent studies that aimed at designing an intuitive and robust Myoelectric Control system for transradial amputees. The methods developed assume that the forearm muscles are Controlled in a synergistic manner and capture this synergistic structure hidden in the electromyographic signal patterns by factorization algorithms. We have shown that this system is capable of providing robust Control over multiple degrees of freedom relying on 6 electrodes only and that it is robust to electrode shift. However, a pure factorization approach may result in some unwanted movements when the user is willing to activate only one function, which is mitigated by combining a synergistic Controller with pattern recognition.
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a novel percutaneous electrode implant for improving robustness in advanced Myoelectric Control
Frontiers in Neuroscience, 2016Co-Authors: Janne M Hahne, Dario Farina, Ning Jiang, David LiebetanzAbstract:Despite several decades of research, electrically powered hand and arm prostheses are still Controlled with very simple algorithms that process the surface electromyogram (EMG) of remnant muscles to achieve Control of one prosthetic function at a time. More advanced machine learning methods have shown promising results under laboratory conditions. However, limited robustness has largely prevented the transfer of these laboratory advances to clinical applications. In this paper, we introduce a novel percutaneous EMG electrode to be implanted chronically with the aim of improving the reliability of EMG detection in Myoelectric Control. The proposed electrode requires a minimally invasive procedure for its implantation, similar to a cosmetic micro-dermal implant. Moreover, being percutaneous, it does not require power and data telemetry modules. Four of these electrodes were chronically implanted in the forearm of an able-bodied human volunteer for testing their characteristics. The implants showed significantly lower impedance and greater robustness against mechanical interference than traditional surface EMG electrodes used for Myoelectric Control. Moreover, the EMG signals detected by the proposed systems allowed more stable Control performance across sessions in different days than that achieved with classic EMG electrodes. In conclusion, the proposed implants may be a promising interface for clinically available prostheses.
Dario Farina - One of the best experts on this subject based on the ideXlab platform.
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HD-EMG to Assess Motor Learning in Myoelectric Control
Converging Clinical and Engineering Research on Neurorehabilitation III, 2018Co-Authors: Sigrid S.g. Dupan, Ivan Vujaklija, Dario Farina, Giulia De Vitis, Strahinja Dosen, Dick F. StegemanAbstract:Online Myoelectric Control involves two types of adaptation: computational adaptation, in which the Controller learns to associate muscle patterns with performed forces; and behavioural adaptation, where the users learn the new interface, and adapt their motor Control strategies based on the errors they observe. In order to study the behavioural motor learning during online Myoelectric Control, twelve able-bodied participants performed single and 2-finger presses through force and Myoelectric Control. Myoelectric Control was obtained with linear ridge regression, and was based on a training set only containing single finger presses. The distance between muscle patterns of force and EMG Control trials indicated that motor learning leads to changes in neural drive, even on the trained presses. This suggests that motor learning is an integral part of Myoelectric Control, where the ability of the user to learn the EMG-to-force mapping impacts the overall performance of the Myoelectric Controller.
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robust extraction of basis functions for simultaneous and proportional Myoelectric Control via sparse non negative matrix factorization
Journal of Neural Engineering, 2018Co-Authors: Chuang Lin, Ning Jiang, Binghui Wang, Dario FarinaAbstract:Objective. This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) Myoelectric Control method for active prostheses. Approach. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the Control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently. Main Results. In online testing based on target hitting, able-bodied subjects reached a greater throughput (TP) when using sparse NMF (SNMF) than with classic NMF or with linear regression (LR). Accordingly, the completion time (CT) was shorter for SNMF than NMF or LR. The same observations were made in two patients with unilateral limb deficiencies. Significance. The addition of sparseness constraints to NMF allows for a quasi-unsupervised approach to Myoelectric Control with superior results with respect to previous methods for the simultaneous and proportional Control of multi-DOF. The proposed factorization algorithm allows robust simultaneous and proportional Control, is superior to previous supervised algorithms, and, because of minimal supervision, paves the way to online adaptation in Myoelectric Control.
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Context-dependent adaptation improves robustness of Myoelectric Control for upper-limb prostheses.
Journal of neural engineering, 2017Co-Authors: Gauravkumar K. Patel, Dario Farina, Janne M Hahne, Claudio Castellini, Strahinja DosenAbstract:Objective. Dexterous upper-limb prostheses are available today to restore grasping, but an effective and reliable feed-forward Control is still missing. The aim of this work was to improve the robustness and reliability of Myoelectric Control by using context information from sensors embedded within the prosthesis. Approach. We developed a context-driven Myoelectric Control scheme (cxMYO) that incorporates the inference of context information from proprioception (inertial measurement unit) and exteroception (force and grip aperture) sensors to modulate the outputs of Myoelectric Control. Further, a realistic evaluation of the cxMYO was performed online in able-bodied subjects using three functional tasks, during which the cxMYO was compared to a purely machine-learning-based Myoelectric Control (MYO). Main results. The results demonstrated that utilizing context information decreased the number of unwanted commands, improving the performance (success rate and dropped objects) in all three functional tasks. Specifically, the median number of objects dropped per round with cxMYO was zero in all three tasks and a significant increase in the number of successful transfers was seen in two out of three functional tasks. Additionally, the subjects reported better user experience. Significance. This is the first online evaluation of a method integrating information from multiple on-board prosthesis sensors to modulate the output of a machine-learning-based Myoelectric Controller. The proposed scheme is general and presents a simple, non-invasive and cost-effective approach for improving the robustness of Myoelectric Control.
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A Biologically-Inspired Robust Control System for Myoelectric Control
Converging Clinical and Engineering Research on Neurorehabilitation II, 2016Co-Authors: Silvia Muceli, Ivan Vujaklija, Bernhard Graimann, Ning Jiang, Sebastian Amsuess, Oskar C. Aszmann, Dario FarinaAbstract:We review recent studies that aimed at designing an intuitive and robust Myoelectric Control system for transradial amputees. The methods developed assume that the forearm muscles are Controlled in a synergistic manner and capture this synergistic structure hidden in the electromyographic signal patterns by factorization algorithms. We have shown that this system is capable of providing robust Control over multiple degrees of freedom relying on 6 electrodes only and that it is robust to electrode shift. However, a pure factorization approach may result in some unwanted movements when the user is willing to activate only one function, which is mitigated by combining a synergistic Controller with pattern recognition.
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a novel percutaneous electrode implant for improving robustness in advanced Myoelectric Control
Frontiers in Neuroscience, 2016Co-Authors: Janne M Hahne, Dario Farina, Ning Jiang, David LiebetanzAbstract:Despite several decades of research, electrically powered hand and arm prostheses are still Controlled with very simple algorithms that process the surface electromyogram (EMG) of remnant muscles to achieve Control of one prosthetic function at a time. More advanced machine learning methods have shown promising results under laboratory conditions. However, limited robustness has largely prevented the transfer of these laboratory advances to clinical applications. In this paper, we introduce a novel percutaneous EMG electrode to be implanted chronically with the aim of improving the reliability of EMG detection in Myoelectric Control. The proposed electrode requires a minimally invasive procedure for its implantation, similar to a cosmetic micro-dermal implant. Moreover, being percutaneous, it does not require power and data telemetry modules. Four of these electrodes were chronically implanted in the forearm of an able-bodied human volunteer for testing their characteristics. The implants showed significantly lower impedance and greater robustness against mechanical interference than traditional surface EMG electrodes used for Myoelectric Control. Moreover, the EMG signals detected by the proposed systems allowed more stable Control performance across sessions in different days than that achieved with classic EMG electrodes. In conclusion, the proposed implants may be a promising interface for clinically available prostheses.
Kevin Englehart - One of the best experts on this subject based on the ideXlab platform.
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motion normalized proportional Control for improved pattern recognition based Myoelectric Control
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014Co-Authors: Erik Scheme, Levi J. Hargrove, Blair A Lock, Wendy Hill, Usha Kuruganti, Kevin EnglehartAbstract: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 of deriving proportional Control using a one degree of freedom Fitts' law test for each of the wrist flexion/extension, wrist pronation/supination and hand close/open degrees of freedom. Performance was evaluated using the Fitts' law throughput value as well as more descriptive metrics including path efficiency, overshoot, stopping distance and completion rate. The proposed normalization methods significantly outperformed the incumbent method in every performance category for able bodied subjects and nearly every category for amputee subjects. Furthermore, one proposed method significantly outperformed both other methods in throughput , yielding 21% and 40% improvement over the incumbent method for amputee and able bodied subjects, respectively. The proposed Control schemes represent a computationally simple method of fundamentally improving Myoelectric Control users' ability to elicit robust, and Controlled, proportional velocity commands.
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training strategies for mitigating the effect of proportional Control on classification in pattern recognition based Myoelectric Control
Jpo Journal of Prosthetics and Orthotics, 2013Co-Authors: Erik Scheme, Kevin EnglehartAbstract: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 greatly during functional use. Proportional Control has been shown to greatly improve the usability of conventional Myoelectric Control systems. Typically, a measure of the amplitude of the electromyogram (a rectified and smoothed version) is used to dictate the velocity of Control of a device. The discriminatory power of Myoelectric pattern classifiers, however, is also largely based on amplitude features of the electromyogram. This work presents an introductory look at the effect of contraction strength and proportional Control on pattern recognition based Control. These effects are investigated using typical pattern recognition data collection methods as well as a real-time position tracking test. Training with dynamically force varying contractions and appropriate gain selection is shown to significantly improve (p<0.001) the classifier’s performance and tolerance to proportional Control.
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Training Strategies for Mitigating the Effect of Proportional Control on Classification in Pattern Recognition Based Myoelectric Control.
Journal of prosthetics and orthotics : JPO, 2013Co-Authors: Erik Scheme, Kevin EnglehartAbstract: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 greatly during functional use. Proportional Control has been shown to greatly improve the usability of conventional Myoelectric Control systems. Typically, a measure of the amplitude of the electromyogram (a rectified and smoothed version) is used to dictate the velocity of Control of a device. The discriminatory power of Myoelectric pattern classifiers, however, is also largely based on amplitude features of the electromyogram. This work presents an introductory look at the effect of contraction strength and proportional Control on pattern recognition based Control. These effects are investigated using typical pattern recognition data collection methods as well as a real-time position tracking test. Training with dynamically force varying contractions and appropriate gain selection is shown to significantly improve (p
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confidence based rejection for improved pattern recognition Myoelectric Control
IEEE Transactions on Biomedical Engineering, 2013Co-Authors: Erik Scheme, B. Hudgins, Kevin EnglehartAbstract:This study describes a novel Myoelectric Control scheme that is capable of motion rejection. As an extension of the commonly used linear discriminant analysis (LDA), this system generates a confidence score for each decision, providing the ability to reject those with a score below a selected threshold. The thresholds are class-specific and affect only the rejection characteristics of the associated class. Furthermore, because the rejection stage is implemented using the outputs of the LDA, the active motion classification accuracy of the proposed system is shown to outperform that of the LDA for all values of rejection threshold. The proposed scheme was compared to a baseline LDA-based pattern recognition system using a real-time Fitts' law-based target acquisition task. The use of velocity-based Myoelectric Control using the rejection classifier is shown to obey Fitts' law, producing linear regression fittings with high coefficients of determination (R2 > 0.943). Significantly higher (p <; 0.001) throughput, path efficiency, and completion rates were observed with the rejection-capable system for both able-bodied and amputee subjects.
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Confidence-Based Rejection for Improved Pattern Recognition Myoelectric Control
IEEE transactions on bio-medical engineering, 2013Co-Authors: Erik Scheme, B. Hudgins, Kevin EnglehartAbstract:This study describes a novel Myoelectric Control scheme that is capable of motion rejection. As an extension of the commonly used linear discriminant analysis (LDA), this system generates a confidence score for each decision, providing the ability to reject those with a score below a selected threshold. The thresholds are class-specific and affect only the rejection characteristics of the associated class. Furthermore, because the rejection stage is implemented using the outputs of the LDA, the active motion classification accuracy of the proposed system is shown to outperform that of the LDA for all values of rejection threshold. The proposed scheme was compared to a baseline LDA-based pattern recognition system using a real-time Fitts' law-based target acquisition task. The use of velocity-based Myoelectric Control using the rejection classifier is shown to obey Fitts' law, producing linear regression fittings with high coefficients of determination (R2 > 0.943). Significantly higher (p
P.a. Parker - One of the best experts on this subject based on the ideXlab platform.
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Control of upper limb prostheses terminology and proportional Myoelectric Control a review
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012Co-Authors: Anders Lyngvi Fougner, Yves Losier, Oyvind Stavdahl, Peter J Kyberd, P.a. ParkerAbstract:The recent introduction of novel multifunction hands as well as new Control paradigms increase the demand for advanced prosthetic Control systems. In this context, an unambiguous terminology and a good understanding of the nature of the Control problem is important for efficient research and communication concerning the subject. Thus, one purpose of this paper is to suggest an unambiguous taxonomy, applicable to Control systems for upper limb prostheses and also to prostheses in general. A functionally partitioned model of the prosthesis Control problem is also presented along with the taxonomy. In the second half of the paper, the suggested taxonomy has been exploited in a comprehensive literature review on proportional Myoelectric Control of upper limb prostheses. The review revealed that the methods for system training have not matured at the same pace as the novel multifunction prostheses and more advanced intent interpretation methods. Few publications exist regarding the choice of training method and the composition of the training data set. In this context, the notion of outcome measures is essential. By definition, system training involves optimization, and the quality of the results depends heavily on the choice of appropriate optimization criteria. In order to further promote the development of proportional Myoelectric Control, these topics need to be addressed.
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a wavelet based continuous classification scheme for multifunction Myoelectric Control
IEEE Transactions on Biomedical Engineering, 2001Co-Authors: K Englehart, B Hudgin, P.a. ParkerAbstract:This work represents an ongoing investigation of dexterous and natural Control of powered upper limbs using the Myoelectric signal. When approached as a pattern recognition problem, the success of a Myoelectric Control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of Myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state Myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of Myoelectric Control than one based on discrete, transient bursts of activity.
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A Neural Network Classifier For Multifunction Myoelectric Control
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991, 1Co-Authors: B. Hudgins, P.a. Parker, Robert N. ScottAbstract:This paper discusses the implementation of a multifunction prosthetic limb Control system based on an artificial neural networkclassifier. The new 5-state Myoelectric Control system uses the information found in the first 200 ms of Myoelectric signal following the initiation of a contraction to determine the function state. This state selection scheme is easily integrated with proportional Control to provide the required speed Control of the selected function. Results are presented on the state selection accuracy of the new 5-state system.
