Precise and timely disease diagnosis is essential for making effective treatment decisions
and halting disease progression. Biomedical signals offer the potential for non-invasive …

Myoelectric control has emerged as a promising approach for a wide range of applications,
including controlling limb prosthetics, teleoperating robots and enabling immersive …

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 …

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

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 …

Stereopsis is the ability of human beings to get the 3D perception on real scenarios. The
conventional stereopsis measurement is based on subjective judgment for stereograms …

Despite hand gesture recognition is a widely investigated field, the design of myoelectric
architectures for detecting finer motor task, like the handwriting, is less studied. However …

Objective: Electromyographic (EMG) signals show large variabilities over time due to factors
such as electrode shifting, user behaviour variations, etc., substantially degrading the …

Serious games show a lot of potential for use in movement rehabilitation (eg, after a stroke,
injury to the spinal cord, or limb loss). However, the nature of this research leads to diversity …

Deep neural networks (DNNs) have demonstrated higher performance results when
compared to traditional approaches for implementing robust myoelectric control (MEC) …