The ability to reach, grasp, and lift requires reliable control, a strong understanding of the arm properties, and some feedback to achieve quick and precise movements. These normal everyday activities present a challenge for upper limb amputees. Myoelectric battery-powered prostheses have been used as one approach to tackle this challenge. Myoelectric signals are highly variable signals produced by muscle contractions that require processing before being used to control prostheses' movements. For multiple degrees of freedom motion, myoelectric controllers are either robust but provide inadequate feedback, or noisy but provide rich feedback. Feedback affects both control and the development of internal models, which in turn affects the overall performance of the prostheses. The human brain has an internal model built for the arm, which imitates its behavior, predicts consequences of an action, and computes an action based on desired consequences. Researchers have been so far unable to decouple the feedback from the control, which has forced them to develop control strategies that might enable strong control signals, but at the expense of internal model strength.

The main objective of this work is to effectively decouple feedback from control by using augmented feedback and subsequently independently optimize both control and the internal model. In this work, a novel augmented-feedback myoelectric control strategy is introduced and assessed using psychophysical tests and commonly used performance measures. Results show that this developed controller enables more precise internal models, resulting in better performance than currently available controllers.