AIMC Topic: Electromyography

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Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge?

Journal of neural engineering
OBJECTIVE: Novel rehabilitation strategies apply robot-assisted exercises and neurofeedback tasks to facilitate intensive motor training. We aimed to disentangle task-specific and subject-related contributions to the perceived workload of these inter...

An experimental comparison of the relative benefits of work and torque assistance in ankle exoskeletons.

Journal of applied physiology (Bethesda, Md. : 1985)
Techniques proposed for assisting locomotion with exoskeletons have often included a combination of active work input and passive torque support, but the physiological effects of different assistance techniques remain unclear. We performed an experim...

A neural network that finds a naturalistic solution for the production of muscle activity.

Nature neuroscience
It remains an open question how neural responses in motor cortex relate to movement. We explored the hypothesis that motor cortex reflects dynamics appropriate for generating temporally patterned outgoing commands. To formalize this hypothesis, we tr...

Locomotor Adaptation by Transtibial Amputees Walking With an Experimental Powered Prosthesis Under Continuous Myoelectric Control.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Lower limb amputees can use electrical activity from their residual muscles for myoelectric control of a powered prosthesis. The most common approach for myoelectric control is a finite state controller that identifies behavioral states and discrete ...

Robust muscle activity onset detection using an unsupervised electromyogram learning framework.

PloS one
Accurate muscle activity onset detection is an essential prerequisite for many applications of surface electromyogram (EMG). This study presents an unsupervised EMG learning framework based on a sequential Gaussian mixture model (GMM) to detect muscl...

A Fuzzy Kernel Motion Classifier for Autonomous Stroke Rehabilitation.

IEEE journal of biomedical and health informatics
Autonomous poststroke rehabilitation systems which can be deployed outside hospital with no or reduced supervision have attracted increasing amount of research attentions due to the high expenditure associated with the current inpatient stroke rehabi...

Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
The quantification of non-linear characteristics of electromyography (EMG) must contain information allowing to discriminate neuromuscular strategies during dynamic skills. There are a lack of studies about muscle coordination under motor constrains ...

High-Density Electromyography and Motor Skill Learning for Robust Long-Term Control of a 7-DoF Robot Arm.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Myoelectric control offers a direct interface between human intent and various robotic applications through recorded muscle activity. Traditional control schemes realize this interface through direct mapping or pattern recognition techniques. The for...

An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation.

IEEE transactions on haptics
This paper presents a novel electromyography (EMG)-driven hand exoskeleton for bilateral rehabilitation of grasping in stroke. The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to pa...

Adaptive myoelectric pattern recognition toward improved multifunctional prosthesis control.

Medical engineering & physics
The non-stationary property of electromyography (EMG) signals in real life settings usually hinders the clinical application of the myoelectric pattern recognition for prosthesis control. The classical EMG pattern recognition approach consists of two...