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Electromyography

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A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models.

Sensors (Basel, Switzerland)
Upper limb amputation severely affects the quality of life and the activities of daily living of a person. In the last decade, many robotic hand prostheses have been developed which are controlled by using various sensing technologies such as artific...

Improved and Secured Electromyography in the Internet of Health Things.

IEEE journal of biomedical and health informatics
Physiological signals are of great importance for clinical analysis but are prone to diverse interferences. To enable practical applications, biosignal quality issues, especially contaminants, need to be dealt with automated processes. For example, a...

Using EMG signals to assess proximity of instruments to nerve roots during robot-assisted spinal surgery.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Detecting neural threats using electromyography (EMG) has gained recognition in the field of spinal surgery. To provide an efficient approach to detect neural threats during the operation of the spinal surgery robot, an automated method b...

Recognizing Missing Electromyography Signal by Data Split Reorganization Strategy and Weight-Based Multiple Neural Network Voting Method.

IEEE transactions on neural networks and learning systems
Surface electromyography (sEMG) signals have been applied widely in prosthetic hand controlling. In the sEMG signal acquisition, wireless devices bring convenience, but also introduce signal missing due to interference or failure during data transmis...

Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot.

Sensors (Basel, Switzerland)
Human Machine Interfaces (HMI) principles are for the development of interfaces for assistance or support systems in physiotherapy or rehabilitation processes. One of the main problems is the degree of customization when applying some rehabilitation ...

Machine Learning for Detection of Muscular Activity from Surface EMG Signals.

Sensors (Basel, Switzerland)
BACKGROUND: Muscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not available yet. The aim of this study is to investigate the reliability of a novel machine-learning-base...

EMG-driven fatigue-based self-adapting admittance control of a hand rehabilitation robot.

Journal of biomechanics
Upper-limb rehabilitation therapy sessions for post-stroke people generally contain rhythmic hand movements in a tiresome manner to rebuild the injured neural circuits. Fatigue formation causes breaks in the training and limits the therapy duration. ...

Automated Machine Learning Pipeline Framework for Classification of Pediatric Functional Nausea Using High-Resolution Electrogastrogram.

IEEE transactions on bio-medical engineering
OBJECTIVE: Pediatric functional nausea is challenging for patients to manage and for clinicians to treat since it lacks objective diagnosis and assessment. A data-driven non-invasive diagnostic screening tool that distinguishes the electro-pathophysi...

Decoding finger movement patterns from microscopic neural drive information based on deep learning.

Medical engineering & physics
Recent development of surface electromyogram (sEMG) decomposition technique provides a good basis of decoding movements from individual motor unit (MU) activities that directly representing microscopic neural drives. How to interpret the function and...

Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion.

BMC medical informatics and decision making
PURPOSE: Surface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG) signals with rich information were introduced into sEMG to improve the recognition rate of fatigue ...