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Machine learning in pain research
J Lötsch, A Ultsch - Pain, 2018 - journals.lww.com
Pain and pain chronification are incompletely understood and unresolved medical problems
that continue to have a high prevalence. 14 It has been accepted that pain is a complex …
that continue to have a high prevalence. 14 It has been accepted that pain is a complex …
Control capabilities of myoelectric robotic prostheses by hand amputees: a scientific research and market overview
Hand amputation can dramatically affect the capabilities of a person. Cortical reorganization
occurs in the brain, but the motor and somatosensorial cortex can interact with the remnant …
occurs in the brain, but the motor and somatosensorial cortex can interact with the remnant …
Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands
Natural control methods based on surface electromyography (sEMG) and pattern
recognition are promising for hand prosthetics. However, the control robustness offered by …
recognition are promising for hand prosthetics. However, the control robustness offered by …
Comparison of six electromyography acquisition setups on hand movement classification tasks
Hand prostheses controlled by surface electromyography are promising due to the non-
invasive approach and the control capabilities offered by machine learning. Nevertheless …
invasive approach and the control capabilities offered by machine learning. Nevertheless …
sEMG time–frequency features for hand movements classification
Abstract Surface Electro-MyoGraphic (sEMG) signals recorded on the forearm can provide
information about the hand movement, which can help control a prosthetic implant for …
information about the hand movement, which can help control a prosthetic implant for …
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 …
[HTML][HTML] A Multi-Scale CNN for Transfer Learning in sEMG-Based Hand Gesture Recognition for Prosthetic Devices
Advancements in neural network approaches have enhanced the effectiveness of surface
Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity …
Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity …
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 …
Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics
A hand amputation is a highly disabling event, having severe physical and psychological
repercussions on a person's life. Despite extensive efforts devoted to restoring the missing …
repercussions on a person's life. Despite extensive efforts devoted to restoring the missing …
Classification of transient myoelectric signals for the control of multi-grasp hand prostheses
Understanding the neurophysiological signals underlying voluntary motor control and
decoding them for controlling limb prostheses is one of the major challenges in applied …
decoding them for controlling limb prostheses is one of the major challenges in applied …