Myoelectric signals (MES) have been used in various applications, in particular, for
identification of user intention to potentially control assistive devices for amputees, orthotic …

The increasing amount of data in electromyographic (EMG) signal research has greatly
increased the importance of developing advanced data analysis and machine learning …

Pattern recognition of electromyography (EMG) signals can potentially improve the
performance of myoelectric control for upper limb prostheses with respect to current clinical …

A method for the classification of finger movements for dexterous control of prosthetic hands
is proposed. Previous research was mainly devoted to identify hand movements as these …

Varieties of noises are major problem in recognition of Electromyography (EMG) signal.
Hence, methods to remove noise become most significant in EMG signal analysis. White …

The performance of intelligent electromyogram (EMG)-driven prostheses, functioning as
artificial alternatives to missing limbs, is influenced by several dynamic factors including …

To develop multi-functionalhuman-machine interfaces that can help disabled people
reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) …

In this study, we propose a multiuser myoelectric interface that can easily adapt to novel
users. When a user performs different motions (eg, grasping and pinching), different …

Pattern recognition based myoelectric control systems rely on detecting repeatable patterns
at given electrode locations. This work describes an experiment to determine the effect of …

Recently, wavelet analysis has proved to be one of the most powerful signal processing
tools for the analysis of surface electromyography (sEMG) signals. It has been widely used …