Toward higher-performance bionic limbs for wider clinical use
Most prosthetic limbs can autonomously move with dexterity, yet they are not perceived by
the user as belonging to their own body. Robotic limbs can convey information about the …
the user as belonging to their own body. Robotic limbs can convey information about the …
Real-time EMG based pattern recognition control for hand prostheses: A review on existing methods, challenges and future implementation
Upper limb amputation is a condition that significantly restricts the amputees from performing
their daily activities. The myoelectric prosthesis, using signals from residual stump muscles …
their daily activities. The myoelectric prosthesis, using signals from residual stump muscles …
Artificial tactile perception smart finger for material identification based on triboelectric sensing
Tactile perception includes the direct response of tactile corpuscles to environmental stimuli
and psychological parameters associated with brain recognition. To date, several artificial …
and psychological parameters associated with brain recognition. To date, several artificial …
A soft neuroprosthetic hand providing simultaneous myoelectric control and tactile feedback
Neuroprosthetic hands are typically heavy (over 400 g) and expensive (more than US
$10,000), and lack the compliance and tactile feedback of human hands. Here, we report the …
$10,000), and lack the compliance and tactile feedback of human hands. Here, we report the …
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 …
A highly integrated bionic hand with neural control and feedback for use in daily life
Restoration of sensorimotor function after amputation has remained challenging because of
the lack of human-machine interfaces that provide reliable control, feedback, and …
the lack of human-machine interfaces that provide reliable control, feedback, and …
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 …
A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition
The surface electromyography (sEMG)-based gesture recognition with deep learning
approach plays an increasingly important role in human-computer interaction. Existing deep …
approach plays an increasingly important role in human-computer interaction. Existing deep …
Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions
Objective. Advanced robotic lower limb prostheses are mainly controlled autonomously.
Although the existing control can assist cyclic movements during locomotion of amputee …
Although the existing control can assist cyclic movements during locomotion of amputee …
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 …