Engineers at the University of Utah (UofU), Salt Lake City, have developed and have been trialing a computerized bionic leg to help amputees walk faster, easier, and with better balance. Now, mechanical engineering assistant professor Tommaso Lenzi, PhD, who heads the project developing the Utah Bionic Leg, has received 2 grants to further advance the technology. One is a $2.2 million award from the National Institutes of Health and the other a $600,000 grant from the National Science Foundation
The leg uses custom-designed force and torque sensors as well as accelerometers and gyroscopes to help determine the leg’s position in space. Those sensors are connected to a computer processor that perceives the environment and determines the user’s rhythmic motions, step length, and walking speed. Based on that real-time data, it then provides power to the motors in the ankle and knee joints to assist in walking, standing up, ascending stairs, or maneuvering around obstacles. These systems working in conjunction give the user more power to walk with less stress on the body than with a standard prosthesis. That means people with amputations, particularly elderly individuals, can walk much longer with the prosthesis.
Just as important, the leg weighs about 6 pounds, half the weight of other bionic legs under development. While the prosthesis is made of mostly aluminum and titanium, the lightweight construction is more due to the leg’s design in which “all of the elements play together,” Lenzi said. “We have a unique way of designing the systems.” For example, the leg uses a smart transmission system connecting the electrical motors to the joints. This optimized system intuitively knows what kind of activity the user wants to do and automatically adapts to it, like shifting gears on a bike. The leg also uses smaller batteries to power the motors that are built into the leg.
The grants will be used to research how the leg enables a user to move better and do more. The team will also be researching how the prosthesis could be designed to better anticipate a user’s movements by tracking muscle activity in the person’s residual limb.