Surface Electromyography (EMG)-based pattern recognition methods have been
investigated over the past years as a means of controlling upper limb prostheses. Despite …

Hand gestures can be recognized using the upper limb's electromyography (EMG) that
measures the electrical activity of the skeletal muscles. However, generalization of muscle …

Motions of the fingers are complex since hand grasping and manipulation are conducted by
spatial and temporal coordination of forearm muscles and tendons. The dominant methods …

In myoelectric control, the calculation of a number of time domain features uses a threshold.
However there is no consensus on the choice of the optimal threshold values. In this study …

Real-time classification of Electromyography signals is the most challenging part of
controlling a prosthetic hand. Achieving a high classification accuracy of EMG signals in a …

With the ability to precisely detect muscle deformation, ultrasound sensing has been widely
employed as a promising technique to interpret movement intentions in the field of human …

This work presents the design, implementation and validation of learning techniques based
on the kNN scheme for gesture detection in prosthetic control. To cope with high …

Stroke patients often suffer from severe upper limb paresis. Rehabilitation treatment typically
targets motor impairments as early as possible, however, muscular contractions, particularly …

Electromyogram (EMG) signal is considered as an easy-to-capture (ie skin-mounted) and
promissing biometric for the control of prosthetic hands. Despite the plethora number of …

The EMG signals are being used in electronic systems with biofeedback control for tracking
and classifying of hand motion. These systems present a challenge in identifying the …