Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life,
and limits their performance in activities of daily life. The realization of natural control for …

Electromyography (EMG) has already been broadly used in human-machine interaction
(HMI) applications. Determining how to decode the information inside EMG signals robustly …

In this work, we present a hardware-software solution to improve the robustness of hand
gesture recognition to confounding factors in myoelectric control. The solution includes a …

Electromyography (EMG) has been explored as an HCI input modality following a long
history of success for prosthesis control. While EMG has the potential to address a range of …

Sensors are devices that output signals for sensing physical phenomena and are widely
used in all aspects of our social production activities. The continuous recording of physical …

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

Surface electromyography (sEMG) signals have been used for advanced prosthetics control,
hand-gesture recognition (HGR), and more recently as a novel biometric trait. For these …

The pattern recognition (PR) based on surface electromyography (sEMG) could improve the
quality of daily life of amputees. However, the lack of robustness and adaptability hinders its …

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 …

Despite its rich history of success in controlling powered prostheses and emerging
commercial interests in ubiquitous computing, myoelectric control continues to suffer from a …