Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future
W Li, P Shi, H Yu - Frontiers in neuroscience, 2021 - frontiersin.org
Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life,
and limits their performance in activities of daily life. The realization of natural control for …
and limits their performance in activities of daily life. The realization of natural control for …
Intelligent EMG pattern recognition control method for upper-limb multifunctional prostheses: advances, current challenges, and future prospects
Upper-limb amputation imposes significant burden on amputees thereby restricting them
from fully exploring their environments during activities of daily living. The use of intelligent …
from fully exploring their environments during activities of daily living. The use of intelligent …
Adhesive and hydrophobic bilayer hydrogel enabled on‐skin biosensors for high‐fidelity classification of human emotion
Traditional human emotion recognition is based on electroencephalogram (EEG) data
collection technologies which rely on plenty of rigid electrodes and lack anti‐interference …
collection technologies which rely on plenty of rigid electrodes and lack anti‐interference …
Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals
Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to
control external devices. This paper presents the decoding of intuitive upper extremity …
control external devices. This paper presents the decoding of intuitive upper extremity …
SAE+ LSTM: A new framework for emotion recognition from multi-channel EEG
EEG-based automatic emotion recognition can help brain-inspired robots in improving their
interactions with humans. This paper presents a novel framework for emotion recognition …
interactions with humans. This paper presents a novel framework for emotion recognition …
Motor imagery EEG signals decoding by multivariate empirical wavelet transform-based framework for robust brain–computer interfaces
The robustness and computational load are the key challenges in motor imagery (MI) based
on electroencephalography (EEG) signals to decode for the development of practical brain …
on electroencephalography (EEG) signals to decode for the development of practical brain …
Pattern recognition of electromyography signals based on novel time domain features for amputees' limb motion classification
Feature extraction is essential in Electromyography pattern recognition (EMG-PR) based
prostheses control method. Time-domain features have been shown to have good …
prostheses control method. Time-domain features have been shown to have good …
Brain-machine interfaces for rehabilitation in stroke: a review
E López-Larraz, A Sarasola-Sanz… - …, 2018 - content.iospress.com
BACKGROUND: Motor paralysis after stroke has devastating consequences for the patients,
families and caregivers. Although therapies have improved in the recent years, traditional …
families and caregivers. Although therapies have improved in the recent years, traditional …
Toward robust, adaptiveand reliable upper-limb motion estimation using machine learning and deep learning–A survey in myoelectric control
To develop multi-functionalhuman-machine interfaces that can help disabled people
reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) …
reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) …
NeuroGrasp: Real-time EEG classification of high-level motor imagery tasks using a dual-stage deep learning framework
Brain–computer interfaces (BCIs) have been widely employed to identify and estimate a
user's intention to trigger a robotic device by decoding motor imagery (MI) from an …
user's intention to trigger a robotic device by decoding motor imagery (MI) from an …