Upper limb amputation is a significant cause of disability that drastically limits an individual's functional capabilities and can also have profound psychological and social effects. Although prosthetic devices are currently the best treatment option for upper limb amputation, even the most technologically advanced prosthetic arms fail to adequately restore the functional capabilities of the lost arm. Myoelectric prosthesis control using pattern recognition was first introduced to the commercial market by Coapt, LLC in late 2013. It provides more natural and intuitive control and eliminates the need for mode switching and the requirement for strong and isolated EMG signals from agonist/antagonist muscle sites. However, the current system is limited to providing control of only one prosthesis movement at a time. The need for simultaneous control of multiple movements has been long cited in the literature and is often voiced by clinicians in the field. The proposed project is to finalize and implement a simultaneous control algorithm in Coapt, LLC's next-generation commercial pattern recognition controller. The long-term goal of this application is to advance the field of upper-limb prosthetics by providing a state-of-the-art control system with unprecedented functionality and ease of use.
The specific aims of the proposal are to (1) implement the recently developed simultaneous control algorithm on Coapt's next-generation commercial controller and (2) evaluate the simultaneous control algorithm in a home trial. Under the first aim, the simultaneous control algorithm will be incorporated into the next-generation system's firmware and user interface software and then optimized to minimize processing time. If necessary, hardware updates will be made to accommodate the increased processing load, and the resulting system will be fully tested and validated. Under the second aim, an IRB-approved randomized crossover study will be performed. Participants will use the system with and without simultaneous control capabilities at home for two 8-week trial periods and will perform virtual control tasks and complete a questionnaire at the end of each trial period. The system will also collect usage data during each trial. All data will be analyzed to determine wear-time under each control strategy, preferred control strategy, and frequently selected simultaneous movements. This proposed work is fully expected to result in a commercial product, including the FDA premarket notification process, within a short time of project completion.
The proposed research is relevant to public health because the clinical application of advanced, multifunction, upper-limb prostheses is expected to improve the quality of life for a large population of persons with upper-limb amputation. An improved simultaneous control option for upper-limb amputees that pairs pattern recognition to existing commercial prosthetic devices can dramatically enhance the adoptability of improved devices for those amputees in need.
