Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

We present a framework for gesture customization requiring minimal examples from users,
all without degrading the performance of existing gesture sets. To achieve this, we first …

While tremendous advances in visual and auditory realism have been made for virtual and
augmented reality (VR/AR), introducing a plausible sense of physicality into the virtual world …

Advances in biosignal signal processing and machine learning, in particular Deep Neural
Networks (DNNs), have paved the way for the development of innovative Human-Machine …

Sensing the proper signal could be a vital piece of the solution to the much evading
attributes of prosthetic hands, such as robustness to noise, ease of connectivity, and intuitive …

Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG)
signals have recently shown considerable potential for development of advanced …

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 …

Gesture recognition through surface electromyography (sEMG) provides a new method for
the control algorithm of bionic limbs, which is a promising technology in the field of human …

This work is motivated by potentials of Deep Neural Networks (DNNs)-based solutions in
improving myoelectric control for trustworthy Human-Machine Interfacing (HMI). In this …

Advancements in Biological Signal Processing (BSP) and Machine-Learning (ML) models
have paved the path for development of novel immersive Human-Machine Interfaces (HMI) …