With the joint advancement in areas such as pervasive neural data sensing, neural
computing, neuromodulation and artificial intelligence, neural interface has become a …

Surface electromyogram (sEMG)-based hand gesture recognition for prosthesis or armband
is an important application of the human–machine interface (HMI). However, the …

The poor generalization performance and heavy training burden of the gesture classification
model contribute as two main barriers that hinder the commercialization of sEMG-based …

Extracting robust features from surface electromyogram (sEMG) for accurate pattern
recognition is a central research topic in biomechanics and human-machine interaction …

As a unique physiological electrical signal in the human body, surface electromyography
(sEMG) signals always include human movement intention and muscle state. Through the …

Machine and deep learning techniques have received increasing attentions in estimating
finger forces from high-density surface electromyography (HDsEMG), especially for neural …

Myoelectric pattern recognition (MPR) has evolved into a sophisticated technology widely
employed in controlling myoelectric interface (MI) devices like prosthetic and orthotic robots …

Prosthetic devices are vital for enhancing personal autonomy and the quality of life for
amputees. However, the rejection rate for electric upper-limb prostheses remains high at …

As one of the most direct and pivotal modes of human–computer interaction (HCI), the
application of surface electromyography (sEMG) signals in the domain of gesture prediction …

The existing surface electromyography-based pattern recognition system (sEMG-PRS)
exhibits limited generalizability in practical applications. In this paper, we propose a stacked …