Myoelectric Control of Powered Upper Limb Prostheses

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The loss or congenital absence of a limb is a major disability that can have considerable physical and psychological impact on the life of an amputee. It is estimated that there are more than 3 million upper limb amputees globally, with the rate of incidence growing steadily. Better robotic prostheses can dramatically improve the quality of life for persons with an upper limb amputation, many of whom reject existing devices because they have trouble controlling them in the same intuitive, subconscious way that they controlled their intact arms.***Myoelectric prostheses use the electromyogram (EMG) signals (the electrical signals generated during muscle contraction) from residual limb muscles to control motorized arm joints. The use of EMG offers a non-invasive means of establishing a natural interface to the neuromuscular system to control the lost functions of the limb.******Although significant advances have been made in building lighter, stronger and more versatile prostheses over the last 20 years, little progress has been made in viable control of these prostheses. Individuals who have used myoelectric prostheses clearly indicate that it is the reliability and dexterity of control that is the most significant factor in acceptance of these devices.******The objective of this research is to deliver robust, dexterous control to myoelectric prostheses. Although the applicant, and others, have demonstrated great success in myoelectric control using pattern recognition and regression based methods in controlled laboratory settings, the ability to have these methods succeed in user's homes and work environments requires advances in robustness and dexterity that will translate into a meaningful improvement in user experience. This will be accomplished by introducing innovative modeling and signal processing paradigms, including novel methods in pattern recognition, adaptive learning, and nonlinear regression. **