ArticleFrontiers in neuroscience2017
EMG-Based Continuous and Simultaneous Estimation of Arm Kinematics in Able-Bodied Individuals and Stroke Survivors.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 68 citations in OpenAlex.
- Utilization of Classification Learning Algorithms for Upper-Body Non-Cyclic Motion Prediction.Sensors (Basel, Switzerland) · 2025Article
- Real-Time Gait Intention Recognition for Active Control of Unilateral Knee Exoskeleton.Applied bionics and biomechanics · 2024Article
- Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- EMG-Based Estimation of Lower Limb Joint Angles and Moments Using Long Short-Term Memory Network.Sensors (Basel, Switzerland) · 2023Article
- Effect of somatosensory simulation training combined with EMG biofeedback on upper limb dysfunction in stroke patients.American journal of translational research · 2023Article
- sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning.Sensors (Basel, Switzerland) · 2022Article
- Intra-Subject and Inter-Subject Movement Variability Quantified with Muscle Synergies in Upper-Limb Reaching Movements.Biomimetics (Basel, Switzerland) · 2021Article
- EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot.Sensors (Basel, Switzerland) · 2021Article
- Continuous Myoelectric Prediction of Future Ankle Angle and Moment Across Ambulation Conditions and Their Transitions.Frontiers in neuroscience · 2021Article
- Continuous Multi-DoF Wrist Kinematics Estimation Based on a Human-Machine Interface With Electrical-Impedance-Tomography.Frontiers in neurorobotics · 2021Article
- Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals.Frontiers in public health · 2021Article
- Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network.Journal of medical systems · 2020Article
- Article
- sEMG-Based Trunk Compensation Detection in Rehabilitation Training.Frontiers in neuroscience · 2019Article
- Design and effectiveness evaluation of mirror myoelectric interfaces: a novel method to restore movement in hemiplegic patients.Scientific reports · 2018Article
- A New Nonlinear Autoregressive Exogenous (NARX)-based Intra-spinal Stimulation Approach to Decode Brain Electrical Activity for Restoration of Leg Movement in Spinally-injured Rabbits.Basic and clinical neuroscienceArticle
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Among the potential biological signals for human-machine interactions (brain, nerve, and muscle signals), electromyography (EMG) widely used in clinical setting can be obtained non-invasively as motor commands to control movements. The aim of this study was to develop a model for continuous and simultaneous decoding of multi-joint dynamic arm movements based on multi-channel surface EMG signals crossing the joints, leading to application of myoelectrically controlled exoskeleton robots for upper-limb rehabilitation. Twenty subjects were recruited for this study including 10 stroke subjects and 10 able-bodied subjects. The subjects performed free arm reaching movements in the horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface EMG signals from six muscles crossing the three joints were recorded. A non-linear autoregressive exogenous (NARX) model was developed to continuously decode the shoulder, elbow and wrist movements based solely on the EMG signals. The shoulder, elbow and wrist movements were decoded accurately based only on the EMG inputs in all the subjects, with the variance accounted for (VAF) > 98% for all three joints. The proposed approach is capable of simultaneously and continuously decoding multi-joint movements of the human arm by taking into account the non-linear mappings between the muscle EMGs and joint movements, which may provide less effortful control of robotic exoskeletons for rehabilitation training of individuals with neurological disorders and arm impairment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.