The Space Between

February 21, 2019

 

When Alison Sheets-Singer was a young gymnast in Maryland, her coach, Steve Cornelison, held his day job with the U.S. Army Research Laboratory. Cornelison held a Ph.D. degree in physics and was often executing calculations focused on improving his gymnasts’ performances. She may not have understood much about it at the time, but Sheets-Singer knew that whatever it was that he was working out on paper was leading to suggestions that had a measurable impact. Cornelison could tell her, for example, exactly how much higher she would be able to fly through the air on the vault if she could just run 2% faster down the runway.


He also developed training devices. Take the uneven bars: traditionally, when a gymnast is first learning a new high-bar trick, she isn’t overly concerned about the low bar. In fact, it’s not even there.  


The low bar comes later—once you’ve got the trick down. The gymnast starts with nothing, then adds a string—but even running into that string can be quite uncomfortable. To solve that problem, Cornelison developed a laser that sounded an alarm when the gymnast swung through its beam, thus allowing girls to heed the low bar much earlier and more painlessly in their training.


It wasn’t until high school when Sheets-Singer took physics—and started learning about velocity and angular momentum— that everything her coach was doing began to make sense. When it all came together, she was hooked. Sheets-Singer went to Cornell, where she continued gymnastics and began to look for ways to combine biology, mechanics, and physics.


She graduated in 2000, which placed Sheets-Singer a little ahead of the curve. It wasn’t until her senior year that Cornell made biomechanics a concentration within the Mechanical Engineering Department. She took as many classes as she could.


After Cornell, she headed to the University of California (UC), Davis, for graduate school. There, as she worked toward her Ph.D. degree in sports biomechanics in the Mechanical Engineering Department with Dr. Mont Hubbard, she had the opportunity to work on the computational side of biomechanics. Sheets-Singer was taking computer modeling, simulation, and optimization and applying those techniques to human-performance problems. In her case, she turned back to gymnastics, developing computer simulations to optimize how gymnasts should swing on the uneven bars to achieve maximum flips on their dismount, leaving time to nail a balanced landing. 


Sheets-Singer enjoyed graduate school, but funding it was difficult. “If you’re looking for research funding from the National Institutes of Health, it’s not hard to see why certain decisions are made and why funding for sports performance isn’t high on their list,” she explains.


Still, Sheets-Singer was determined to find a way to develop her career. She had a vision of what her career could be—identifying the limits of human performance for different people—so she really had to take a long-term look at her options. She was a teaching assistant in three different departments at UC Davis, worked outside jobs, and was a race timer and announced at a few triathlons. She did whatever she had to do to get the money to make it through. There was no large grant; it was all piecemeal. There were times, she says, where it would be ten days before the quarter started and she didn’t know from where her funding was going to come.


You’ve Been Served
For her postdoc she headed to Stanford to work with Dr. Tom Andriacchi. Sheets-Singer was looking for more experience in the experimental data-collection side— what she would call “the more human side of biomechanics.” In Andriacchi’s lab, she worked with a team on a markerless motion-tracking system that she applied for kinematic analysis of athletes. Her work focused on one particular set of motions— the serve of tennis players.


This was around 2011, and markerless motion tracking was new. Up until this point, motion capture revolved around the use of motion sensors attached to a moving object. There were limitations to this technology, though—it worked best in a controlled environment, the equipment could hinder intricate natural movement, and it could take a long time to set up. With markerless motion tracking, Andriacchi’s team aimed to bring data collection outside to the tennis courts. NCAA Division I tennis players worked with the researchers, and Sheets-Singer says they put a lot of demands on them. Through a variety of serves—flat, slice, and kick—the team collected information that they hoped would help them quantify the demands the serves put on the athletes and eventually evaluate the risk of shoulder injuries.


Sheets-Singer switched gears when she took a faculty position in the Mechanical Engineering Department at The Ohio State University for a few years. There she set up a lab to continue her work on markerless tracking, but she moved from human to more general animal performance—mice.
She partnered with neuroscience researcher Dr. Michele Basso in the School of Health and Rehabilitation Sciences to study rehabilitation therapies to promote the recovery of mobility after spinal-cord injuries. The goal of their partnership was to create more sensitive and quantitative methods of measuring movement outcomes following rehab interventions.


Her team developed the measurement methods to evaluate the effectiveness of Basso’s techniques, such as exercise protocols, testing them in mice models to determine when and how a person recovering from an injury should exercise to best promote healing.


Just Do It
It wasn’t long until Nike sought her out. At Nike, Sheets-Singer continues to push the limits of human performance. She works at a Nike Sports Research lab of about 60 people—a mix of biomechanists, physiologists, data scientists, engineers, and many other technologists and scientists. The goal of the research lab, she says, is “...to make athletes quantifiably better by working with product development teams to create innovative products and services.” In other words—how can researchers apply the basic laws of mechanics to help us understand how people move?


In this group, it’s all about how the product and the particular athlete interact, so, as Alison describes it, they might start with this “black box” of the human body. Then they develop theories of how muscles and tendons work together, how much energy a particular athlete needs to move in a particular way, and how the team can improve performance. They might apply different interventions to see if these theories are correct. Researchers will revise the theory as many times as necessary, and that knowledge drives new innovations, until, perhaps, the outcomes of the research appear in the Nike products you see on the shelf.


Sheets-Singer’s job helps athletes achieve their full potential. This is what she, as an athlete, loves—developing a product that goes out to the public. “In the end, the consumer decides—it’s how we measure success,” she explains. “If it makes them more comfortable, or if makes them run faster, jump higher, or perform better in any way, well, that’s pretty exciting.”


It would have been really easy to quit, especially in graduate school. One of the factors that kept Sheets-Singer going was her amazing mentors and a great support network that was always there to remind her of the career that she wanted and that helped her see that she could make it through.
 

This article first appeared in IEEE Women in Engineering Magazine December 2017.

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