DWT-based electromyogram signal classification using maximum likelihood-estimated features for neurodiagnostic applications
Sign Up to like & getrecommendations! Published in 2020 at "Signal, Image and Video Processing"
DOI: 10.1007/s11760-019-01590-6
Abstract: Automated diagnosis of neuromuscular disorders such as myopathy and neuropathy can be done by measuring and analyzing the nonlinear and non-stationary trends in electromyogram (EMG) signals. This paper introduces a new automated diagnostic approach with… read more here.
Keywords: classification; dwt based; maximum likelihood; based electromyogram ... See more keywords
A new optical flow model for motor unit conduction velocity estimation in multichannel surface EMG
Sign Up to like & getrecommendations! Published in 2017 at "Computers in biology and medicine"
DOI: 10.1016/j.compbiomed.2017.02.006
Abstract: Many studies have demonstrated the feasibility and benefits of Conduction Velocity (CV) estimation from surface electromyograms (EMGs) in various experimental conditions. Among them, a method based on optical flow was proposed recently, demonstrating relatively accurate… read more here.
Keywords: estimation; emg; velocity estimation; conduction velocity ... See more keywords
Percentage estimation of muscular activity of the forearm by means of EMG signals based on the gesture recognized using CNN
Sign Up to like & getrecommendations! Published in 2020 at "Sensing and bio-sensing research"
DOI: 10.1016/j.sbsr.2020.100353
Abstract: Abstract Within muscle activity based on surface electromyographic (EMG) signals, the percentage estimate of muscle activation, which is the level of intensity with which muscles have been activated, has not been exploited. This work presents… read more here.
Keywords: estimation; percentage estimation; gesture; emg signals ... See more keywords
Toward a generalizable deep CNN for neural drive estimation across muscles and participants
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Neural Engineering"
DOI: 10.1088/1741-2552/acae0b
Abstract: Objective. High-density electromyography (HD-EMG) decomposition algorithms are used to identify individual motor unit (MU) spike trains, which collectively constitute the neural code of movements, to predict motor intent. This approach has advanced from offline to… read more here.
Keywords: emg signals; neural drive; drive estimation; cnn ... See more keywords
Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3225761
Abstract: Wrist electromyography (EMG) signals have been explored for incorporation into subtle wrist-worn wearable devices for decoding hand gestures. Previous studies have now shown that wrist EMG can even outperform the more commonly used forearm EMG,… read more here.
Keywords: emg signals; day; emg; forearm emg ... See more keywords
Development of Consumer-Friendly Surface Electromyography System for Muscle Fatigue Detection
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3237557
Abstract: In this study, a low-cost, wireless, and smartphone-controlled surface electromyography (EMG) system was designed and developed for consumers, and the recorded EMG signals were evaluated against a reference laboratory EMG system during fatiguing contraction. Using… read more here.
Keywords: emg signals; system; consumer friendly; emg ... See more keywords
Comparing EMG Continuous Movement Decoding With Joints Unconstrained and Constrained
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3191533
Abstract: Electromyography (EMG) signals have been employed for continuous movement decoding in recent years. However, several studies demonstrated that due to physiological factors, the EMG signals of amputees were poorer with respect to that of the… read more here.
Keywords: continuous movement; emg signals; movement; mcp wrist ... See more keywords
Deep Learning for Robust Decomposition of High-Density Surface EMG Signals
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Biomedical Engineering"
DOI: 10.1109/tbme.2020.3006508
Abstract: Blind source separation (BSS) algorithms, such as gradient convolution kernel compensation (gCKC), can efficiently and accurately decompose high-density surface electromyography (HD-sEMG) signals into constituent motor unit (MU) action potential trains. Once the separation matrix is… read more here.
Keywords: density surface; high density; emg signals; decomposition ... See more keywords
Diagnosis of Neuromuscular Disorders Using DT-CWT and Rotation Forest Ensemble Classifier
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2019.2918596
Abstract: Electromyographic (EMG) signals are utilized to analyze the neuromuscular disorders. Machine learning algorithms have been employed as a decision support system to detect neuromuscular disorders. EMG signals contain noise from different sources, such as electrical… read more here.
Keywords: emg signals; forest ensemble; neuromuscular disorders; rotation forest ... See more keywords
Hardware Implementation for Lower Limb Surface EMG Measurement and Analysis Using Explainable AI for Activity Recognition
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2022.3198443
Abstract: Electromyography (EMG) signals are gaining popularity for several biomedical applications, including pattern recognition, disease detection, human–machine interfaces, medical image processing, and robotic limb or exoskeleton fabrication. In this study, a two-channel data acquisition system for… read more here.
Keywords: emg signals; limb; activity recognition; lower limb ... See more keywords
Comparing EMG-Based Human-Machine Interfaces for Estimating Continuous, Coordinated Movements
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Neural Systems and Rehabilitation Engineering"
DOI: 10.1109/tnsre.2019.2937929
Abstract: Electromyography (EMG)-based interfaces are trending toward continuous, simultaneous control with multiple degrees of freedom. Emerging methods range from data-driven approaches to biomechanical model-based methods. However, there has been no direct comparison between these two types… read more here.
Keywords: emg signals; based human; emg based; emg ... See more keywords