Feature validation and online visualization of forearm high-density EMG in an individual with spinal cord injury J. Sebastian Correaa, Jordyn E. Tinga, Devapratim Sarmab, Douglas J. Webera, b Department of Bioengineering, University of Pittsburgh Department of Physical Medicine and Rehabilitation, University of Pittsburgh a
b
Sebastian Correa is a bioengineering and Spanish student from Pittsburgh, PA. After graduating, he aspires to combine his passions for neural engineering and improving global health through his future career. Sebastian Correa
Douglas Weber, Ph.D. is an Associate Professor in the Department of Bioengineering, and he holds secondary appointments in Physical Medicine and Rehabilitation and Electrical Engineering. He is also a faculty member in the Center for the Neural Basis of Cognition, the Douglas Weber, PhD University of Pittsburgh Brain Institute, and the McGowan Institute for Regenerative Medicine. He established the Rehab Neural Engineering Lab in 2005 when he joined the University of Pittsburgh.
Significance Statement
Myoelectric signals can be recorded from the clinically paralyzed muscles of individuals who have been affected by spinal cord injury. With the optimization of signal processing, there is the potential to significantly improve the quality-of-life of patients by allowing them to control assistive devices through the use of these myoelectric signals.
Category: Experimental research
Keywords: High-density electromyography, signal feature extraction, spinal cord injury
30 Undergraduate Research at the Swanson School of Engineering
Abstract
Spinal cord injury (SCI) results in damage to the corticospinal tract, weakening electrically active muscles that generate functional movements. The weak myoelectric signals produced by paretic (weakened) muscles due to SCI can be recorded through high-density electromyography (HDEMG) and used to understand pathological changes related to the injury. A custom HDEMG sleeve was used to measure electromyographic (EMG) signals from the forearms of participants with tetraplegia. Recorded EMG signals were filtered and processed to produce a set of signal features, including the root-mean-square, zero-crossings, and power. These features were used to quantify the strength of activation in forearm muscles which can allow us to discriminate activity patterns associated with different hand movements. The purpose of this study is to optimize the method of processing HDEMG signals with the future goal of enabling people with SCI to intuitively control assistive devices using EMG signals from their clinically paralyzed muscles.
1. Introduction 1.1 Motivation Every year about 18,000 people in the United States are directly affected by a spinal cord injury (SCI). This amounts to nearly 300,000 people living with spinal cord injuries in the U.S. [1]. This traumatic injury can cause life-long damage to several areas of the body, including motor and sensory impairments. One of the main components in the spinal cord that is responsible for the movement of the limbs is the corticospinal tract. Damage to the corticospinal tract can leave patients with paralysis. Paralysis in all four limbs is known as tetraplegia. As a result, affected individuals are often unable to independently perform activities of daily living and are therefore reliant on caretakers. In this study, we aim to help restore functional movements to individuals affected by SCI through the use of EMG-controlled assistive devices. 1.2 Using Electromyography to Classify Hand Movements Damage to the corticospinal tract weakens electrically active muscles responsible for performing functional movements. Impaired muscle fibers discharge spontaneously, or not at all, due to the pathological deficits found in the spinal cord. This hindered signal is what causes muscles to become paralyzed. The electrical potentials generated by muscle fibers, known as myoelectric signals, can be measured using electrodes placed at the surface of the skin. This method is known as surface electromyography (EMG) and is commonly used in clinical applications. High-density EMG (HDEMG) electrode arrays have been developed to measure these signals with a higher spatial resolution than traditional EMG devices. These systems use a large number of tightly spaced electrodes to provide a highdensity coverage across the surface of the limb. This allows for a more accurate assessment of deficits in people affected by neuromuscular disorders such as SCI. Through the processing of these myoelectric signals, there is the potential for the use of this technology to accurately control
