Electromyography (EMG) signals are becoming increasingly important in many applications,
including clinical/biomedical, prosthesis or rehabilitation devices, human machine …

Upper limb amputation is a condition that significantly restricts the amputees from performing
their daily activities. The myoelectric prosthesis, using signals from residual stump muscles …

Abstract Recently, Convolutional Neural Networks (CNNs) have been used for the
classification of hand activities from surface Electromyography (sEMG) signals. However …

User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of
feedback, and difficult training are cited as primary reasons. Recently, researchers have …

Several efforts have been carried out to enhance dexterous hand prosthesis control by
impaired individuals. Choosing which voluntary signal to use for control purposes is a critical …

The goal of supervised feature selection is to find a subset of input features that are
responsible for predicting output values. The least absolute shrinkage and selection …

Identifying a subset of features that preserves classification accuracy is a problem of growing
importance, because of the increasing size and dimensionality of real-world data sets. We …

Currently, most of the adopted myoelectric schemes for upper limb prostheses do not
provide users with intuitive control. Higher accuracies have been reported using different …

Abstract Despite high classification accuracies (~ 95%) of myoelectric control systems based
on pattern recognition, it is unclear how well offline measures translate to real-time closed …

A real-time pattern recognition algorithm based on k-nearest neighbors and lazy learning
was used to classify, voluntary electromyography (EMG) signals and to simultaneously …