“Impairment of hand function is a common outcome after neurological injury such as stroke and spinal cord injury,” says Dr. Zhou, Professor of Physical Medicine and Rehabilitation at McGovern Medical School at UTHealth and Director of the NeuroMyo Engineering for Rehabilitation Laboratory at the TIRR Memorial Hermann Research Center. “Patients and clinicians face many challenges in hand function rehabilitation, especially in regaining fine motor skills such as finger and thumb movements.”
The success of robotic-assisted training in rehabilitation after neurologic injury has led to the development of electromyography-driven robots and exoskeletons that help patients perform hand exercises actively with intention. The devices extract information from EMG signals gathered through electrodes placed strategically; the signals reflect the inherent activity patterns of muscles.
“Active robotic-aided training that requires input from the patient has been shown to have a positive therapeutic effect and promote motor learning, increasing the importance of intention in this type of rehabilitation,” Dr. Zhou says. “Myoelectric pattern control has been used to improve prosthesis control for limb-loss patients, particularly in combination with a surgical technique called targeted muscle reinnervation. Our research team presented a novel framework using high-density surface EMG recording and pattern recognition analysis for different neurological injury populations. Based on the results of earlier studies, we know that substantial neural control information is contained in the muscles affected by neurologic injury, and we believe it can be extracted using advanced EMG signal-processing techniques. This information offers a potentially powerful approach to controlling rehabilitative or assistive devices.”
Dr. Zhou’s current study builds on research published in Frontiers in Neurology,1 American Journal of Physical Medicine and Rehabilitation2 and the International Journal of Neural Systems3 and examines the restoration of hand functions such as grasping, pinching and releasing objects in stroke patients and in spinal cord injury patients. His research with stroke patients is funded by the American Heart Association/American Stroke Association; Mission Connect, a program of TIRR Foundation, is funding the study in patients with SCI. Ten stroke patients and 10 spinal cord injury patients will be enrolled. He credits postdoctoral fellow Zhiyuan Lu, PhD, with the bulk of work with study participants and data analysis.
“Myoelectric control of robot-aided hand training requires the patient to be actively engaged throughout the therapy session,” says Dr. Lu. “We hope the study will result in a novel approach to improving hand function in patients recovering from stroke and spinal cord injury that can be used in therapy centers around the world.”
1Lu Z, Tong K, Shin H, Li S, Zhou P. Advanced Myoelectric Control for Robotic Hand-Assisted Training: Outcome for a Stroke Patient. Front Neurol. 2017;89:107. Epub 2017 Mar 20.
2Lu Z, Tong K, Shin H, Stampas A, Zhou P. Robotic Hand-Assisted Training for Spinal Cord Injury Drivern by Myoelectric Pattern Recognition: A Case Report. Am J Phys Med Rehabil. 2017 Oct;96 (10 Suppl):S146-S149.
3Lu Z, Chen X, Zhang X, Tong K, Zhou P. Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition. Int J Neural Syst. 2017 Aug;27(5):1750009. Epub 2016 Oct 8.