A University of Texas at Dallas professor applied robot control theory to enable powered prosthetics to dynamically respond to the wearer’s environment and help amputees walk. As reported in IEEE Transactions on Robotics, wearers of the robotic leg could walk on a moving treadmill nearly as fast as an able-bodied person.
“We borrowed from robot control theory to create a simple, effective new way to analyze the human gait cycle,” said Robert Gregg, PhD, an assistant professor of bioengineering and mechanical engineering. “Our approach resulted in a method for controlling powered prostheses for amputees to help them move in a more stable, natural way than current prostheses.”
While prostheses have been made lighter and more flexible, they fail to mimic the power generated from human muscles. And, powered prostheses use motors to generate force, but lack the intelligence to stably respond to disturbances or changing terrain, the researchers said.
Gregg proposed a new way to view and study the process of human walking by measuring a single variable to represent the motion of the body: the center of pressure on the foot, which moves from heel to toe through the gait cycle.
“We used advanced mathematical theorems to simplify the entire gait cycle down to one variable. If you measure that variable, you know exactly where you are in the gait cycle and exactly what you should be doing,” Gregg said.
He first tested his theory on computer models, and then with three above-knee amputee participants at the Rehabilitation Institute of Chicago (RIC), an affiliate of Northwestern University. RIC supplied an experimental robotic prosthetic leg to each study participant. The control algorithm required a measurement of the length of the residual limb. He explained that a “test socket” was custom made for each participant in the RIC prosthetics shop to ensure an ideal fit.
Gregg implemented his algorithms with sensors measuring the center of pressure on a powered prosthesis. Inputted with only the user’s height, weight, and dimension of the residual thigh into his algorithm, the prosthesis was configured separately for each subject. Subjects then walked on the ground and on a treadmill moving at increasing speeds.
“We did not tell the prosthesis that the treadmill speed was increasing. The prosthesis responded naturally just as the biological leg would do,” Gregg said.
While current powered prosthetic devices require physical rehabilitation specialists spending significant amounts of time tuning hundreds of knobs and training each powered leg to the individual wearer, he explained. “Our approach unified multiple modes of operation into one.”
One of the biggest lessons learned, came not from the device, but from the users. Gregg explained: “Despite all our efforts to debug every possible problem with the robotic leg before participants used it, they were really good at finding new bugs in the system. One participant actually tried his best to confuse the leg! That taught me a valuable lesson about designing and testing medical devices: the user may not treat the device as nicely as the designer.”
To view a video of the technology in action, visit www.techbriefs.com/tv/prosthetic-control.