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Cross-Comparison of EMG-to-Force Methods for Multi-DoF Finger Force Prediction Using One-DoF Training
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Motor unit physiology: Some unresolved issues
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A critical evaluation of the force control hypothesis in motor control
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Identification of dynamic myoelectric signal-to-force models during isometric lumbar muscle contractions
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EMG signal filtering based on Empirical Mode Decomposition
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Current state of digital signal processing in myoelectric interfaces and related applications
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Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates
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Myoelectric signal processing for control of powered limb prostheses
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EMG–force modeling using parallel cascade identification
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Quadriceps EMG/force relationship in knee extension and leg press
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Single site electromyograph amplitude estimation
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Relating agonist-antagonist electromyograms to joint torque during isometric, quasi-isotonic, nonfatiguing contractions
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Myoelectric Signal Processing: Optimal Estimation Applied to Electromyography - Part I: Derivation of the Optimal Myoprocessor
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Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis
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Enhanced Dynamic EMG-Force Estimation Through Calibration and PCI Modeling
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Influence of Joint Angle on EMG-Torque Model During Constant-Posture, Torque-Varying Contractions
inferredcites →https://doi.org/10.1109/tnsre.2016.2639443openalex-snapshot
Comparison of Constant-Posture Force-Varying EMG-Force Dynamic Models About the Elbow
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Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings
inferredcites →https://doi.org/10.1152/jappl.1983.54.3.714openalex-snapshot
Determination of forces in extensor pollicis longus and flexor pollicis longus of the thumb
inferredcites →https://doi.org/10.1152/jn.00936.2006openalex-snapshot
Bayesian Filtering of Myoelectric Signals
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LSTM neural networks for language modeling
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Long-Term Finger Force Predictions Using Motoneuron Discharge Activities
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Robust Feature and Channel Selection for HD-sEMG-Based Finger Force Estimation Using Regression and Deep Learning Models
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Real-Time sEMG Pattern Recognition of Multiple-Mode Movements for Artificial Limbs Based on CNN-RNN Algorithm
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