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Electromyography

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Adapting myoelectric control in real-time using a virtual environment.

Journal of neuroengineering and rehabilitation
BACKGROUND: Pattern recognition technology allows for more intuitive control of myoelectric prostheses. However, the need to collect electromyographic data to initially train the pattern recognition system, and to re-train it during prosthesis use, a...

Evolving Gaussian Process Autoregression Based Learning of Human Motion Intent Using Improved Energy Kernel Method of EMG.

IEEE transactions on bio-medical engineering
Continuous human motion intent learning may be modeled using a Gaussian process (GP) autoregression based evolving system to cope with the unspecified and time-varying motion patterns. Electromyography (EMG) signals are the primary input. GP is used ...

Myoelectric control algorithm for robot-assisted therapy: a hardware-in-the-loop simulation study.

Biomedical engineering online
BACKGROUND: A direct blow to the knee is one way to injure the anterior cruciate ligament (ACL), e.g., during a football or traffic accident. Robot-assisted therapy (RAT) rehabilitation, simulating regular walking, improves walking and balance abilit...

Deep Learning for Musculoskeletal Force Prediction.

Annals of biomedical engineering
Musculoskeletal models permit the determination of internal forces acting during dynamic movement, which is clinically useful, but traditional methods may suffer from slowness and a need for extensive input data. Recently, there has been interest in ...

Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware.

Journal of neural engineering
OBJECTIVE: The objective of this work is to use the capability of spiking neural networks to capture the spatio-temporal information encoded in time-series signals and decode them without the use of hand-crafted features and vector-based learning and...

Classification of needle-EMG resting potentials by machine learning.

Muscle & nerve
INTRODUCTION: The diagnostic importance of audio signal characteristics in needle electromyography (EMG) is well established. Given the recent advent of audio-sound identification by artificial intelligence, we hypothesized that the extraction of cha...

Muscle fatigue assessment during robot-mediated movements.

Journal of neuroengineering and rehabilitation
BACKGROUND: Several neuromuscular disorders present muscle fatigue as a typical symptom. Therefore, a reliable method of fatigue assessment may be crucial for understanding how specific disease features evolve over time and for developing effective r...

A Convenient Non-harm Cervical Spondylosis Intelligent Identity method based on Machine Learning.

Scientific reports
Cervical spondylosis (CS), a most common orthopedic diseases, is mainly identified by the doctor's judgment from the clinical symptoms and cervical change provided by expensive instruments in hospital. Owing to the development of the surface electrom...

Comparison of Muscular Activity and Movement Performance in Robot-Assisted and Freely Performed Exercises.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
End-effector-based robotic systems are, in particular, suitable for extending physical therapy in stroke rehabilitation. An adequate therapy and thus the recovery of movement can only be guaranteed if the physiological muscular activation and movemen...

A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition.

PloS one
The surface electromyography (sEMG)-based gesture recognition with deep learning approach plays an increasingly important role in human-computer interaction. Existing deep learning architectures are mainly based on Convolutional Neural Network (CNN) ...