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Electromyography

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A Deep Q-Network based hand gesture recognition system for control of robotic platforms.

Scientific reports
Hand gesture recognition (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) has been investigated for human-machine applications in the last few years. The information obtained from the HGR systems has the pot...

Decoding Silent Speech Based on High-Density Surface Electromyogram Using Spatiotemporal Neural Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Finer-grained decoding at a phoneme or syllable level is a key technology for continuous recognition of silent speech based on surface electromyogram (sEMG). This paper aims at developing a novel syllable-level decoding method for continuous silent s...

Recognition of Hand Gestures Based on EMG Signals with Deep and Double-Deep Q-Networks.

Sensors (Basel, Switzerland)
In recent years, hand gesture recognition (HGR) technologies that use electromyography (EMG) signals have been of considerable interest in developing human-machine interfaces. Most state-of-the-art HGR approaches are based mainly on supervised machin...

New Artificial Intelligence-Integrated Electromyography-Driven Robot Hand for Upper Extremity Rehabilitation of Patients With Stroke: A Randomized, Controlled Trial.

Neurorehabilitation and neural repair
BACKGROUND: An artificial intelligence (AI)-integrated electromyography (EMG)-driven robot hand was devised for upper extremity (UE) rehabilitation. This robot detects patients' intentions to perform finger extension and flexion based on the EMG acti...

Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design.

Journal of neuroengineering and rehabilitation
BACKGROUND: Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising appro...

A myoelectric digital twin for fast and realistic modelling in deep learning.

Nature communications
Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and ...

Modulation of corticospinal excitability related to the forearm muscle during robot-assisted stepping in humans.

Experimental brain research
In recent years, the neural control mechanisms of the arms and legs during human bipedal walking have been clarified. Rhythmic leg stepping leads to suppression of monosynaptic reflex excitability in forearm muscles. However, it is unknown whether an...

Affect and stress detection based on feature fusion of LSTM and 1DCNN.

Computer methods in biomechanics and biomedical engineering
The impact of emotions on health, especially stress, is receiving increasing attention. It is important to provide a non-invasive affect detection system that can be continuously monitored for a long period of time. Multi-sensor fusion strategies can...

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots.

Sensors (Basel, Switzerland)
The lack of intuitive and active human-robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position ...

Myoelectric Pattern Recognition Using Gramian Angular Field and Convolutional Neural Networks for Muscle-Computer Interface.

Sensors (Basel, Switzerland)
In the field of the muscle-computer interface, the most challenging task is extracting patterns from complex surface electromyography (sEMG) signals to improve the performance of myoelectric pattern recognition. To address this problem, a two-stage a...