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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 …
Progress in Neuroengineering for brain repair: New challenges and open issues
Background: In recent years, biomedical devices have proven to be able to target also
different neurological disorders. Given the rapid ageing of the population and the increase of …
different neurological disorders. Given the rapid ageing of the population and the increase of …
A subject-transfer framework based on single-trial EMG analysis using convolutional neural networks
In recent years, electromyography (EMG)-based practical myoelectric interfaces have been
developed to improve the quality of daily life for people with physical disabilities. With these …
developed to improve the quality of daily life for people with physical disabilities. With these …
Domain adaptation for sEMG-based gesture recognition with recurrent neural networks
Surface Electromyography (sEMG) is to record muscles' electrical activity from a restricted
area of the skin by using electrodes. The sEMG-based gesture recognition is extremely …
area of the skin by using electrodes. The sEMG-based gesture recognition is extremely …
Improving sEMG-based motion intention recognition for upper-limb amputees using transfer learning
Hand gesture recognition from multi-channel surface electromyography (sEMG) have been
widely studied in the past decade. By analyzing muscle activities measured from forearm …
widely studied in the past decade. By analyzing muscle activities measured from forearm …
A quantitative taxonomy of human hand grasps
Background A proper modeling of human gras** and of hand movements is fundamental
for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that …
for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that …
[HTML][HTML] Improving motion intention recognition for trans-radial amputees based on sEMG and transfer learning
C Lin, X Niu, J Zhang, X Fu - Applied Sciences, 2023 - mdpi.com
Hand motion intentions can be detected by analyzing the surface electromyographic (sEMG)
signals obtained from the remaining forearm muscles of trans-radial amputees. This …
signals obtained from the remaining forearm muscles of trans-radial amputees. This …
Muscle synergy analysis of a hand-grasp dataset: a limited subset of motor modules may underlie a large variety of grasps
Background: Kinematic and muscle patterns underlying hand grasps have been widely
investigated in the literature. However, the identification of a reduced set of motor modules …
investigated in the literature. However, the identification of a reduced set of motor modules …
State of the art methods of machine learning for prosthetic hand development: a review
In develo** countries, a number of upper limb incidence that leads to trauma and
amputation is increasing. The development of prosthetic hands has been carried out by …
amputation is increasing. The development of prosthetic hands has been carried out by …
Questioning domain adaptation in myoelectric hand prostheses control: An inter-and intra-subject study
One major challenge limiting the use of dexterous robotic hand prostheses controlled via
electromyography and pattern recognition relates to the important efforts required to train …
electromyography and pattern recognition relates to the important efforts required to train …