[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 …

Neurorobotic fusion of prosthetic touch, kinesthesia, and movement in bionic upper limbs promotes intrinsic brain behaviors

PD Marasco, JS Hebert, JW Sensinger, DT Beckler… - Science robotics, 2021 - science.org
Bionic prostheses have restorative potential. However, the complex interplay between
intuitive motor control, proprioception, and touch that represents the hallmark of human …

A CNN-LSTM hybrid model for wrist kinematics estimation using surface electromyography

T Bao, SAR Zaidi, S Xie, P Yang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Convolutional neural network (CNN) has been widely exploited for simultaneous and
proportional myoelectric control due to its capability of deriving informative, representative …

[HTML][HTML] DARPA investment in peripheral nerve interfaces for prosthetics, prescriptions, and plasticity

S Naufel, GL Knaack, R Miranda, TK Best… - Journal of Neuroscience …, 2020 - Elsevier
Background. Peripheral nerve interfaces have emerged as alternative solutions for a variety
of therapeutic and performance improvement applications. The Defense Advanced …

[HTML][HTML] Neural feedback strategies to improve grasping coordination in neuromusculoskeletal prostheses

E Mastinu, LF Engels, F Clemente, M Dione, P Sassu… - Scientific reports, 2020 - nature.com
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 …

Inter-subject domain adaptation for CNN-based wrist kinematics estimation using sEMG

T Bao, SAR Zaidi, S Xie, P Yang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Recently, convolutional neural network (CNN) has been widely investigated to decode
human intentions using surface Electromyography (sEMG) signals. However, a pre-trained …

Recurrent convolutional neural networks as an approach to position-aware myoelectric prosthesis control

HE Williams, AW Shehata, MR Dawson… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
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 …

[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 …

A deep Kalman filter network for hand kinematics estimation using sEMG

T Bao, Y Zhao, SAR Zaidi, S Xie, P Yang… - Pattern Recognition …, 2021 - Elsevier
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 …

[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 …