Machine learning-based myoelectric control is regarded as an intuitive paradigm, because
of the mapping it creates between muscle co-activation patterns and prosthesis movements …

Surface Electromyography (sEMG) plays a key role in many applications such as control of
Human-Machine Interfaces (HMI) and neuromusculoskeletal modeling. It has strongly …

The ultimate goal of machine learning-based myoelectric control is simultaneous and
independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial …

The inputs to the outputs of nonlinear systems can be modeled using machine and deep
learning approaches, among which artificial neural networks (ANNs) are a promising option …

Abstract We have employed Kernel Least Square Tracker (KRLS-T), a nonlinear kernel
based recursive algorithm, to estimate 3 dimensional wrist kinematics from sEMG signals of …

Despite recent advances in prosthetics and assistive robotics in general, robust
simultaneous and proportional control of dexterous prosthetic devices remains an unsolved …

With commercial prosthetic hands, executing some everyday movements, for example,
concurrent grasp and bending of the wrist to pick up an object from a high shelf, is very …

Myoelectric hand prostheses using pattern recognition control scheme lack simultaneous
motion, and perform robotic unnatural inter-pattern motion. Accordingly, the use of …

We aim to develop a paradigm for simultaneous and independent control of multiple
degrees of freedom (DOFs) for upper-limb prostheses. To that end, we introduce action …

Modern, commercially available hand prostheses offer the potential of individual digit
control. However, this feature is often not utilized due to the lack of a robust scheme for …