On the Classification of Electromyography Signals to Control a Four Degree-Of-Freedom Prosthetic Device.

This study investigates the applicability of Electromyography (EMG) signal classification algorithms with relatively low training time to control prosthetic devices. The perceived quality of control depends on many factors, such as the 1) accuracy of the algorithm, 2) the complexity of the control, and 3) the ability to compensate for the error. The high granularity of control in the time domain reduces the perceived effect of error but also limits the classification accuracy.

Human force discrimination during active arm motion for force feedback design.

The goal of this study was to analyze the human ability of external force discrimination while actively moving the arm. With the approach presented here, we give an overview for the whole arm of the just-noticeable differences (JNDs) for controlled movements separately executed for the wrist, elbow, and shoulder joints. The work was originally motivated in the design phase of the actuation system of a wearable exoskeleton, which is used in a teleoperation scenario where force feedback should be provided to the subject.

Human-like reflex control for an artificial hand.

In this paper, we illustrate the low level reflex control used to govern an anthropomorphic artificial hand. The paper develops the position and stiffness control strategy based on dynamic artificial neurons able to simulate the neurons acting in the human reflex control. The controller has a hierarchical structure.