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Northwestern University Feinberg School of Medicine

Student Q&A: Medical Scientist Training Program

Adenike Adewuyi

Adenike Adewuyi found the challenges that partial hand amputees face fascinating. As a Medical Scientist Training Program (MSTP) student in the Center for Bionic Medicine at the Shirley Ryan AbilityLab, under the mentorship of Todd Kuiken, MD, PhD, and Levi Hargrove, PhD, she based her PhD thesis project on this challenge. Adewuyi is working to develop algorithms to improve the control of artificial limbs.


What is your educational background?
I earned my bachelor's degree in engineering sciences at Harvard University and I'm in my fifth year of the MSTP at Feinberg. I've completed two years of medical school and am in my third year of the PhD program.

What are your research interests? 
My thesis project focuses on creating algorithms for partial hand prostheses and improving control of powered, myoelectric partial hand prostheses.

In our lab, we use a machine learning algorithm to learn the patterns of electric signals that the muscles produce. When you open your hand, muscles in the forearm generate one pattern of electric signals. Closing your hand generates a different pattern of electric signals. Even though upper-limb amputees may be missing part of their arm, their muscles still generate these electric signals when they think about moving their hand and fingers. 

Once a computer “learns” the patterns of muscle electrical activity associated with certain hand movements, we can give it a pattern of electrical muscle signals to determine what the person intended to do. This means that an amputee can control a prosthesis just by thinking about the action. We record these signals, the computer determines what motion the amputee is trying to do and sends the information to the prosthesis, which moves according to the user’s intentions. 

Unlike individuals with amputations in the arm or forearm, partial-hand amputees retain movement in the wrist. When using pattern recognition control, the old algorithm worked well in one wrist position—patients could do the grasps I asked them to do. But if I asked them to do the same thing using a different wrist position, they couldn’t do it; it’s a change in the way the muscles work and the signals produced are different. 

The goal of my project to develop a way to control the prosthesis with pattern-recognition but still allow the amputee to use their own wrist. 

How did you choose this research project?  
I chose this project because it is clinically orientated, and I get to interact with a diverse group of people including surgeons, engineers, orthotists, prosthetists, and therapists. I also love that it merges my background in engineering and interests in neuromotor control.

Why did you pick Kuiken's lab for your research?  
I was looking for something both computational and clinically relevant. I talked to Sandra Lee in the MSTP office, who suggested Kuiken's lab. I ended up working in this lab because it was a great balance of clinically-relevant and engineering research in a diverse and uniquely cooperative environment.

What do you like to do outside of the lab?  
Outside of research I like to sing, dance, play piano, and I'm very active in my church as a deacon. I volunteer with PRISM, a program started by MSTP students. PRomoting Inner-City Youth in Science and Medicine (PRISM) encourages inner-city youth to explore science and medicine careers opportunities. I volunteer because I love interacting with younger students, and teaching them about how totally incredible science and engineering can be.