AIMC Topic: Electromyography

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The concepts of muscle activity generation driven by upper limb kinematics.

Biomedical engineering online
BACKGROUND: The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the e...

LSTM-AE for Domain Shift Quantification in Cross-Day Upper-Limb Motion Estimation Using Surface Electromyography.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Although deep learning (DL) techniques have been extensively researched in upper-limb myoelectric control, system robustness in cross-day applications is still very limited. This is largely caused by non-stable and time-varying properties of surface ...

Continuous online prediction of lower limb joints angles based on sEMG signals by deep learning approach.

Computers in biology and medicine
Continuous online prediction of human joints angles is a key point to improve the performance of man-machine cooperative control. In this study, a framework of online prediction method of joints angles by long short-term memory (LSTM) neural network ...

Explainable and Robust Deep Forests for EMG-Force Modeling.

IEEE journal of biomedical and health informatics
Machine and deep learning techniques have received increasing attentions in estimating finger forces from high-density surface electromyography (HDsEMG), especially for neural interfacing. However, most machine learning models are normally employed a...

A membership-function-based broad learning system for human-robot interaction force estimation under drawing task.

Medical & biological engineering & computing
Estimating interaction force is of great significance in the field of human-robot interaction (HRI) thanks to its guarantee of interaction safety. To this end, this paper proposes a novel estimation method by leveraging broad learning system (BLS) an...

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies.

Scientific reports
The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the ch...

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...