Hand gestures are crucial for develo** prosthetic and rehabilitation devices, enabling
intuitive human–computer interaction (HCI) and improving accessibility for individuals with …

Accurate surface electromyography decoding of hand gestures is pivotal for advancing
human–computer interaction applications. Recent developments in end-to-end deep neural …

Surface Electromyography (sEMG) has become an essential tool in various fields, including
prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent …

Recently, surface electromyographic (sEMG) hand gesture recognition faces a serious
challenge of limited training data in various scenarios. Numerous efforts have been made to …

In recent years, hand gesture recognition in human-computer interfaces is usually based on
surface electromyography because the signals are non-intrusive and are not affected by the …

Machine learning on electromyography (EMG) has recently achieved remarkable success
on various tasks, while such success relies heavily on the assumption that the training and …

The battlefield environment is changing rapidly, and fast and accurate identification of the
tactical intention of enemy targets is an important condition for gaining a decision-making …

Estimation of hand kinematics from surface electromyographic (sEMG) signals provides a
non-invasive human-machine interface. This approach is usually subject-specific, so that the …

Currently, the fusion of surface Electromyography (EMG) and deep learning is gradually
showing immense potential in the research of Lower Limb Motion Intention Recognition …

Objective. Most existing machine learning models for myoelectric control require a large
amount of data to learn user-specific characteristics of the electromyographic (EMG) signals …