On the feasibility of using pattern recognition based myoelectric control as a human-computer interface for individuals with paralysis

Abstract

Human-computer interfaces (HCIs), using electromyogram (EMG) data for control, has been studied for decades as a potential means of restoring functional ability to amputees. Often, these HCIs are used to control a powered prosthesis. However, this technology has potential application outside of the scope of prosthetics. The EMG produced by people with neurological damage could contain enough discriminatory information to distinguish between many classes of motion, including those that they cannot functionally perform. In this study, 10 individuals with spinal cord injuries (SCIs) around the C3-C6 level (ASIA A-C) volunteered to have their EMG studied while performing 10 different classes of motion with their dominant upper limb. Preliminary studies, using high-density EMG, were performed on two volunteers before moving on to using an electrode cuff with 8 bipolar channels. Performing pattern recognition, for the 10 classes, using an LDA classifier referencing 5 features (sample entropy, mean absolute value, zero crossings, slope sign change, and wave length) resulted in a total mean accuracy of 91.5%. This accuracy was increased to 98.0% when evaluating a set of 5 classes. These 5 classes were chosen based on the classes available by the Bioness H200 device, which uses functional stimulation to force user contractions. Such a device could benefit from an accurate EMG controller.