Online Electromyographic Control of a Robotic Prosthesis

This paper presents a two-part study investigating the use of forearm surface electromyographic (EMG) signals for real-time control of a robotic arm. In the first part of the study, we explore and extend current classification-based paradigms for myoelectric control to obtain high accuracy (92-98%) on an eight-class offline classification problem, with up to 16 classifications/s. This offline study suggested that a high degree of control could be achieved with very little training time (under 10 min). The second part of this paper describes the design of an online control system for a robotic arm with 4 degrees of freedom. We evaluated the performance of the EMG-based real-time control system by comparing it with a keyboard-control baseline in a three-subject study for a variety of complex tasks.

Classification of themyoelectric signal usin…Classification of the myoelectric signal using time-frequency based representationsLIBSVM: A library forsupport vector machinesLIBSVM: A library for support vector machinesLearning with KernelsLearning with KernelsEvaluation of theforearm EMG signal…Evaluation of the forearm EMG signal features for the control of a prosthetic handContinuous multifunctionmyoelectric control…Continuous multifunction myoelectric control using pattern recognitionProbability Estimatesfor Multi-Class…Probability Estimates for Multi-Class Classification by Pairwise CouplingA robust, real-timecontrol scheme for…A robust, real-time control scheme for multifunction myoelectric controlLearning With Kernels:Support Vector Machines…Learning With Kernels: Support Vector Machines, Regularization, Optimization, and BeyondContinuous myoelectriccontrol for powered…Continuous myoelectric control for powered prostheses using hidden Markov modelsReal-Time Classificationof Electromyographic…Real-Time Classification of Electromyographic Signals for Robotic ControlA Gaussian mixture modelbased classification…A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prosthesesLibsvm : A library forsupport vector machinesLibsvm : A library for support vector machinesEMG pattern recognitionand grasping force…EMG pattern recognition and grasping force estimation: Improvement to the myocontrol of multi-DOF prosthetic handsFluctuating emg signals:Investigating long-term…Fluctuating emg signals: Investigating long-term effects of pattern matching algorithmsOnline MyoelectricControl of a Dexterous…Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial AmputeesTarget AchievementControl Test: Evaluatin…Target Achievement Control Test: Evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prosthesesBioPatRec: A modularresearch platform for…BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithmsReal-Time, SimultaneousMyoelectric Control…Real-Time, Simultaneous Myoelectric Control Using Force and Position-Based Training ParadigmsBilinear Modeling of EMGSignals to Extract…Bilinear Modeling of EMG Signals to Extract User-Independent Features for Multiuser Myoelectric InterfaceApplication of aself-enhancing…Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis controlThe role of musclesynergies in myoelectri…The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction controlEMG-based decoding ofgrasp gestures in…EMG-based decoding of grasp gestures in reaching-to-grasping motionsMultiday Evaluation ofTechniques for EMG-Base…Multiday Evaluation of Techniques for EMG-Based Classification of Hand MotionsEMGHandNet: A hybrid CNNand Bi-LSTM architectur…EMGHandNet: A hybrid CNN and Bi-LSTM architecture for hand activity classification using surface EMG signalsOnline ElectromyographicControl of a Robotic…Online Electromyographic Control of a Robotic ProsthesisEarlier referencesFocus paperCiting papersOlderNewer

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