[HTML][HTML] A review of sensory feedback in upper-limb prostheses from the perspective of human motor control
JW Sensinger, S Dosen - Frontiers in neuroscience, 2020 - frontiersin.org
This manuscript reviews historical and recent studies that focus on supplementary sensory
feedback for use in upper limb prostheses. It shows that the inability of many studies to …
feedback for use in upper limb prostheses. It shows that the inability of many studies to …
Neurorobotic fusion of prosthetic touch, kinesthesia, and movement in bionic upper limbs promotes intrinsic brain behaviors
Bionic prostheses have restorative potential. However, the complex interplay between
intuitive motor control, proprioception, and touch that represents the hallmark of human …
intuitive motor control, proprioception, and touch that represents the hallmark of human …
A CNN-LSTM hybrid model for wrist kinematics estimation using surface electromyography
Convolutional neural network (CNN) has been widely exploited for simultaneous and
proportional myoelectric control due to its capability of deriving informative, representative …
proportional myoelectric control due to its capability of deriving informative, representative …
[HTML][HTML] DARPA investment in peripheral nerve interfaces for prosthetics, prescriptions, and plasticity
Background. Peripheral nerve interfaces have emerged as alternative solutions for a variety
of therapeutic and performance improvement applications. The Defense Advanced …
of therapeutic and performance improvement applications. The Defense Advanced …
[HTML][HTML] Neural feedback strategies to improve grasping coordination in neuromusculoskeletal prostheses
Conventional prosthetic arms suffer from poor controllability and lack of sensory feedback.
Owing to the absence of tactile sensory information, prosthetic users must rely on incidental …
Owing to the absence of tactile sensory information, prosthetic users must rely on incidental …
Inter-subject domain adaptation for CNN-based wrist kinematics estimation using sEMG
Recently, convolutional neural network (CNN) has been widely investigated to decode
human intentions using surface Electromyography (sEMG) signals. However, a pre-trained …
human intentions using surface Electromyography (sEMG) signals. However, a pre-trained …
Recurrent convolutional neural networks as an approach to position-aware myoelectric prosthesis control
Objective: Persons with normal arm function can perform complex wrist and hand
movements over a wide range of limb positions. However, for those with transradial …
movements over a wide range of limb positions. However, for those with transradial …
[HTML][HTML] Mechanotactile sensory feedback improves embodiment of a prosthetic hand during active use
AW Shehata, M Rehani, ZE Jassat… - Frontiers in …, 2020 - frontiersin.org
There have been several advancements in the field of myoelectric prostheses to improve
dexterity and restore hand grasp patterns for persons with upper limb loss, including robust …
dexterity and restore hand grasp patterns for persons with upper limb loss, including robust …
A deep Kalman filter network for hand kinematics estimation using sEMG
In human-machine interfaces (HMI), deep learning (DL) techniques such as convolutional
neural networks (CNN), long-short term memory networks (LSTM) and the hybrid CNN …
neural networks (CNN), long-short term memory networks (LSTM) and the hybrid CNN …
[HTML][HTML] Application research on optimization algorithm of sEMG gesture recognition based on light CNN+ LSTM model
D Bai, T Liu, X Han, H Yi - Cyborg and bionic systems, 2021 - spj.science.org
The deep learning gesture recognition based on surface electromyography plays an
increasingly important role in human-computer interaction. In order to ensure the high …
increasingly important role in human-computer interaction. In order to ensure the high …