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Electromyography

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Comparing the Lower-Limb Muscle Activation Patterns of Simulated Walking Using an End-Effector-Type Robot with Real Level and Stair Walking in Children with Spastic Bilateral Cerebral Palsy.

Sensors (Basel, Switzerland)
Cerebral palsy is a neurologic disorder caused by lesions on an immature brain, often resulting in spasticity and gait abnormality. This study aimed to compare the muscle activation patterns of real level and stair walking with those of simulated wal...

Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals.

Scientific reports
Designing efficient and labor-saving prosthetic hands requires powerful hand gesture recognition algorithms that can achieve high accuracy with limited complexity and latency. In this context, the paper proposes a Compact Transformer-based Hand Gestu...

High-density Surface and Intramuscular EMG Data from the Tibialis Anterior During Dynamic Contractions.

Scientific data
Valid approaches for interfacing with and deciphering neural commands related to movement are critical to understanding muscular coordination and developing viable prostheses and wearable robotics. While electromyography (EMG) has been an established...

Liquid Metal Flexible EMG Gel Electrodes for Gesture Recognition.

Biosensors
Gesture recognition has been playing an increasingly important role in the field of intelligent control and human-computer interaction. Gesture recognition technology based on electromyography (EMG) with high accuracy has been widely applied. However...

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...