Extracting Simultaneous and Proportional Neural Control Information for Multiple-DOF Prostheses From the Surface Electromyographic Signal

By Ning Jiang, Kevin B. Englehart, Philip A. Parker. IEEE Transactions on Biomedical Engineering, 2009

432 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/0fc8a88c5503660b88d6958113cea80130c945c2

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In Biomedical Engineering

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Autonomy in Rehabilitation Robotics: An Intersection (2018, neutral): “For example, such approaches might have each decoded class map to a set of multi-DoF motions (51) or force functions for each joint (52), or employ direct control within a reduced dimensionality space (53).”
The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges (2014, methodology): “The factorization of multichannel EMG signals by NMF has been proven to provide control signals (the activation signals) that can be used by subjects online to intuitively control multiple DoFs, simultaneously and proportionally [35], [77], [82], [83].”
Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. (2011, neutral): “It is unlikely that robust, simulta neous, independent control of multiple degrees of free dom is attainable for the per son with transradial ampu tation using surfa ce EMG because of the complex nature of forearm muscle synergies [78–79], the inherent cross talk in the surface signal [75,80], and t”
Myoelectric forearm prostheses: state of the art from a user-centered perspective. (2011, background): “and others recently included proportional control in the experimental setup [47–49].”
Myoelectric Control of Artificial Limbs¿Is There a Need to Change Focus? [In the Spotlight] (2012, background): “Further improvements can be achieved by additional processing steps, such as majority vote or feature dimensionality reduction.”
Classification of Simultaneous Movements using Surface EMG Pattern Recognition (2012, methodology): “derlying muscle synergies have also been used to predict the movement of multiple wrist DOFs [13], [14] or static postures involving multiple finger DOFs [15].”
Resolving the Limb Position Effect in Myoelectric Pattern Recognition (2011, methodology): “In the case of simultaneous proportional control systems, such as those described by Jiang [24] and Fougner [25], they could be even more useful.”