Power Reduction of an Embedded Pattern Recognition Myoelectric Control System

Coapt, Llc, Chicago IL

Investigators

Abstract

DESCRIPTION (provided by applicant): Pattern recognition (PR) myoelectric control systems can dramatically improve an amputee patient's control over a powered prostheses, but they have not been made commercially available. Coapt, LLC, is a start-up stage company that is seeking to commercialize PR myoelectric control for the benefit of upper-limb amputees. The controller is based on research completed by the Rehabilitation Institute of Chicago's Center for Bionic Medicine. Numerous challenges, including clinically viable training and recalibration routines, have been overcame within the past decade, yet some still remain. One such challenge is to lower the power requirements of an embedded PR control system to an acceptable level such that the controller does not quickly drain the onboard prosthesis battery. The objective of this application is to lower the power requirements of the PR embedded control system such that it uses less than 10% of the battery's energy over a full day's use. Energy reduction is a critical item in the development of embedded control systems as it is directly related to the weight of the battery which must be attached to the prosthesis and carried by the patient whenever wearing the device. We will demonstrate the technical feasibility of the approach by the following two specific aims: (1) Develop power management algorithms to minimize power consumption of our pattern recognition controller in a manner that is transparent to the user, (2) Implement the power management techniques on the firmware of our pattern recognition controller. It is very important that our power management act in a manner that is transparent to the user. They must allow the overall control system to remain responsive to the user's command, even after long periods during which the system remains dormant. The proposed research is significant because it removes one of the few remaining clinical barriers preventing pattern recognition-based myoelectric control from being adopted. Ultimately, this research will help allow thousands of upper limb amputees to dramatically improve the control their prostheses, allowing for an improved quality of life.