A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration
Electromyography (EMG) signal is one of the widely used biological signals for human motor
intention prediction, which is an essential element in human-robot collaboration systems …
intention prediction, which is an essential element in human-robot collaboration systems …
Principles of motor unit physiology evolve with advances in technology
Movements are generated by the coordinated activation of motor units. Recent technological
advances have made it possible to identify the concurrent activity of several tens of motor …
advances have made it possible to identify the concurrent activity of several tens of motor …
A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition
Wearable devices that monitor muscle activity based on surface electromyography could be
of use in the development of hand gesture recognition applications. Such devices typically …
of use in the development of hand gesture recognition applications. Such devices typically …
Deep learning for electromyographic hand gesture signal classification using transfer learning
In recent years, deep learning algorithms have become increasingly more prominent for
their unparalleled ability to automatically learn discriminant features from large amounts of …
their unparalleled ability to automatically learn discriminant features from large amounts of …
Analysis and biophysics of surface EMG for physiotherapists and kinesiologists: Toward a common language with rehabilitation engineers
Recent decades have seen a move toward evidence-based medicine to inform the clinical
decision-making process with reproducible findings from high-quality research studies …
decision-making process with reproducible findings from high-quality research studies …
Transfer learning for sEMG hand gestures recognition using convolutional neural networks
U Côté-Allard, CL Fall… - … on Systems, Man …, 2017 - ieeexplore.ieee.org
In the realm of surface electromyography (sEMG) gesture recognition, deep learning
algorithms are seldom employed. This is due in part to the large quantity of data required for …
algorithms are seldom employed. This is due in part to the large quantity of data required for …
A convolutional neural network for robotic arm guidance using sEMG based frequency-features
Recently, robotics has been seen as a key solution to improve the quality of life of amputees.
In order to create smarter robotic prosthetic devices to be used in an everyday context, one …
In order to create smarter robotic prosthetic devices to be used in an everyday context, one …
Wearable monitoring devices for biomechanical risk assessment at work: Current status and future challenges—A systematic review
A Ranavolo, F Draicchio, T Varrecchia… - International journal of …, 2018 - mdpi.com
Background: In order to reduce the risk of work-related musculoskeletal disorders (WMSDs)
several methods have been developed, accepted by the international literature and used in …
several methods have been developed, accepted by the international literature and used in …
Converging robotic technologies in targeted neural rehabilitation: a review of emerging solutions and challenges
Recent advances in the field of neural rehabilitation, facilitated through technological
innovation and improved neurophysiological knowledge of impaired motor control, have …
innovation and improved neurophysiological knowledge of impaired motor control, have …
Fundamental concepts of bipolar and high-Density surface EMG understanding and teaching for clinical, occupational, and sport applications: Origin, detection, and …
Surface electromyography (sEMG) has been the subject of thousands of scientific articles,
but many barriers limit its clinical applications. Previous work has indicated that the lack of …
but many barriers limit its clinical applications. Previous work has indicated that the lack of …