Grant: Discovery Grants Program - Individual

The loss of one or both arms is a major disability that profoundly limits the everyday capabilities and interactions of individuals with upper-limb amputation. There are approximately 10,000 patients in Canada with an upper limb amputation. A suitable prosthetic replacement of the amputated limb(s) would enhance their ability to care for themselves, enable a more diverse range of employment, and provide more opportunities to enjoy an active lifestyle. 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. Expectations for control of upper limb prostheses are very high however, because of the standard established by able-bodied dexterity. Although significant advances have been made in building lighter, stronger and more versatile prostheses for clinical use over the last 20 years, little progress made in clinically viable control of these prostheses. Patients 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 clinically robust, dexterous control to myoelectric prostheses. Although great success has been demonstrated (by the applicant, and others) in myoelectric control using pattern recognition methods in controlled laboratory settings, the ability to have these methods succeed clinically (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 new training and signal processing paradigms, including novel methods in pattern recognition, adaptive learning, and signal segmentation.