UA Little Rock Student Wins Award to Research Pattern Recognition Classifiers for Prosthetic Hand Users

When it comes to prosthetic technology, scientists and engineers want to make these devices as natural and seamless as possible for the user, even down to the electrical signals that tell the devices how they should move. Shelby Wingate, a junior majoring in Systems Engineering at UA Little Rock, has received $2,750 from the Arkansas Department of Education’s Student Undergraduate Research Fellowship for her project to research pattern recognition classifiers that analyze electromyography (EMG) signals for hand motion intention.

When we want to move our hands, the brain sends a signal to the muscles to determine how it wants to be moved. This complex neurological function needs to be emulated with users who need prosthetic hand technology. Since the neurological signals can no longer communicate with a hand, a prosthetic hand needs to receive complex signals so it can act naturally for the user. EMG, which is most commonly used to diagnose neurological and neuromuscular disorders, can be used to record these electrical signals in muscle tissue. Wingate and her mentor, Dr. Kamran Iqbal from the UA Little Rock Department of Systems Engineering, will analyze neural signals during hand movements by applying pattern recognition classifiers, a technique that will recognize specific patterns and relationships between neural signals and hand movements.

During her year of research, Wingate, who has a strong background in biology and chemistry, will explore how motor neurons will perform in hand movements. Next, she will build a model of the arm movements that employ muscle synergies, the coordinated movements of groups of muscles. She will use the BioPatRec software research platform to record the neural signals and apply the pattern recognition classifiers. Dr. Gannon White from the UA Little Rock School of Counseling, Human Performance, and Rehabilitation will act as co-supervisor during the experimental phase of the project.

Because of Wingate’s research, she will better understand what is needed for natural arm movement for those who rely on prosthetic hand technology. 10,000 new patients each year must rely on prosthetic technology for upper extremities due to injury or disease. This technology will enable them to perform daily functions naturally.

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