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Electromyography

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Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques.

Medical & biological engineering & computing
The main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface...

Learning to teleoperate an upper-limb assistive humanoid robot for bimanual daily-living tasks.

Biomedical physics & engineering express
Bimanual humanoid platforms for home assistance are nowadays available, both as academic prototypes and commercially. Although they are usually thought of as daily helpers for non-disabled users, their ability to move around, together with their dext...

Advanced Compliant Anti-Gravity Robot System for Lumbar Stabilization Exercise Using Series Elastic Actuator.

IEEE journal of translational engineering in health and medicine
: The lumbar stabilization exercise is one of the most recommended treatments in medical professionals for patients suffering from low back pain. However, because lumbar stabilization exercise is calisthenics, it is challenging to perform because of ...

The Impact of Load Style Variation on Gait Recognition Based on sEMG Images Using a Convolutional Neural Network.

Sensors (Basel, Switzerland)
Surface electromyogram (sEMG) signals are widely employed as a neural control source for lower-limb exoskeletons, in which gait recognition based on sEMG is particularly important. Many scholars have taken measures to improve the accuracy of gait rec...

Towards a Resilience to Stress Index Based on Physiological Response: A Machine Learning Approach.

Sensors (Basel, Switzerland)
This study proposes a new index to measure the resilience of an individual to stress, based on the changes of specific physiological variables. These variables include electromyography, which is the muscle response, blood volume pulse, breathing rate...

Spatio-temporal warping for myoelectric control: an offline, feasibility study.

Journal of neural engineering
The efficacy of an adopted feature extraction method directly affects the classification of the electromyographic (EMG) signals in myoelectric control applications. Most methods attempt to extract the dynamics of the multi-channel EMG signals in the ...

Estimation of Lower Limb Kinematics during Squat Task in Different Loading Using sEMG Activity and Deep Recurrent Neural Networks.

Sensors (Basel, Switzerland)
The aim of the present study was to predict the kinematics of the knee and the ankle joints during a squat training task of different intensities. Lower limb surface electromyographic (sEMG) signals and the 3-D kinematics of lower extremity joints we...

Synergy-Based Neural Interface for Human Gait Tracking With Deep Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Neural information decomposed from electromyography (EMG) signals provides a new path of EMG-based human-machine interface. Instead of the motor unit decomposition-based method, this work presents a novel neural interface for human gait tracking base...

Causal decoding of individual cortical excitability states.

NeuroImage
Brain responsiveness to stimulation fluctuates with rapidly shifting cortical excitability state, as reflected by oscillations in the electroencephalogram (EEG). For example, the amplitude of motor-evoked potentials (MEPs) elicited by transcranial ma...

Information fusion and multi-classifier system for miner fatigue recognition in plateau environments based on electrocardiography and electromyography signals.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Human factors are important contributors to accidents, especially human error induced by fatigue. In this study, field tests and analyses were conducted on physiological indexes extracted from electrocardiography (ECG) and e...