NC researchers develop bionic leg that 'listens' to user

CorrespondentJanuary 26, 2014 

For 39-year-old Richard Watson of Lumberton, walking uphill is no problem. It’s coming back down that’s the trouble.

Watson had to have his leg amputated in 2009 after it got caught in a machine at work.

He is one of more than a million people in the United States who have lost a leg to accident or disease. For many of them, everyday activities like climbing stairs or getting in and out of a car takes concentration and conscious effort.

Their sound leg automatically adjusts to different speeds, activities or types of terrain, but their prosthetic leg drags behind them unless they “tell” it what to do.

Most prosthetic legs in use today are passive, which means the user has to pull it along, or shift weight to lock and unlock the knee.

Others move with help from motorized knee and ankle joints that require pushing a button or toggling a switch to transition between sitting, standing and walking modes.

The result can be awkward and slow-going, even after months of training.

Toward the next step

“The limitation of current prosthetic devices is that they don’t know what the next step should be,” said Helen Huang, an associate professor of biomedical engineering at N.C. State and UNC-Chapel Hill.

With help from a $1.2 million grant from the National Science Foundation, Huang and her colleagues are building a smarter, safer bionic leg that “listens” to the user’s body and figures out what he or she has in mind before that person takes the next step.

The engineers’ hope is that one day, people who have lost all or part of a leg will be able to transition smoothly and seamlessly from sitting to standing to navigating stairs and slopes – and even regain their balance to avoid falls after slips and trips – using only their thoughts.

It may sound like the stuff of science fiction. But advances in brain-to-machine interfaces are turning mind-controlled bionic limbs into a reality.

The technology uses sensors capable of detecting the same brain and muscle signals the original leg used.

When an amputee thinks about bending his knee, the part of the brain that controls movement sends a tiny electrical signal down the spinal cord to peripheral nerves in his residual limb, just as it did before he lost his leg.

Only now, instead of reaching a dead end at the site of amputation, the signal is picked up by electrodes in the prosthetic leg.

Within milliseconds, pattern-recognition software in the device’s onboard computer decodes the signals and translates them into instructions that tell the knee joint how to move to stay in sync with the wearer.

Thought-controlled cursors and other robotic devices have been around for some time. Some rely on electrodes that sit on the surface of the skin. Others use electrodes that are surgically implanted in the muscles or beneath the skull.

Testing a prototype

The noninvasive approach can’t match the accuracy and precision of surgical methods, but by combining skin surface signals from multiple sources, Huang hopes to narrow the gap.

Huang and her team have already built a prototype of a bionic leg that uses skin sensors to measure electrical signals produced by the muscles in the user’s residual limb, and they have been testing it in the lab to see how it performs.

The volunteers – all people with above-knee amputations – take off the artificial leg they normally wear, put on the bionic one, and run through a series of exercises.

In one study, the amputees maneuvered through an obstacle course that involved stepping over a box, climbing five stairs or walking up a 10-foot ramp, and then traversing the course in reverse.

By combining muscle signals with data from motion and force sensors that measured the angle and the rotation of the knee and the pounding on the leg with each step, the artificial leg was able to correctly anticipate the next move 95 percent to 99 percent of the time.

Since then, Huang and her team have improved the technology by adding a “stumble detector” that helps amputees regain their balance after slips and trips.

To test the idea, the researchers asked amputees to walk on a treadmill that stopped and started suddenly to throw them off balance.

The volunteers wore a harness suspended from the ceiling to keep them from actually falling to the floor.

As they walked, sensors measured the electrical activity of their thigh muscles. Motion sensors in the artificial leg detected when they began to slip or trip.

By combining muscle signals with data from the motion sensors, the researchers were able to figure out what combination of signals allowed the artificial leg to act fast enough and accurately enough to prevent a fall.

What Huang wants to do now is build a bionic leg that combines muscle signals with signals from the brain.

The approach could be especially useful in cases where there is too little muscle left in the residual limb to rely on muscle signals alone.

“By using brain signals, we might be able to compensate for that,” Huang said.

The brain factor

On a table in her lab, a Styrofoam mannequin wears something that looks like a futuristic swimming cap.

The cap is lined with 64 sensors that detect brain electrical activity via electroencephalography (EEG).

Huang and her team will ask amputees and able-bodied volunteers to wear the cap and record their brain signals while they do basic moves, like bending the knee.

Brain signals are hard to measure noninvasively because they have to pass through the skull and cranial fluid before they reach the scalp.

It’s like recording a cocktail party by placing microphones on the outside of a building. Some people are closer to some microphones than others, and each microphone picks up a different mix of voices. Teasing out individual conversations from the rest of the noise is tricky.

The researchers’ first task is to combine the signals from the many sensors in the cap and see if they can identify distinctive patterns of brain activity related to specific movements.

Eventually, the goal is to program the bionic leg to recognize a particular pattern of brain activity as a specific command to, say, lift the foot or bend the knee.

In the meantime, Watson has been using the device to walk up and down an incline in Huang’s lab. He won’t be able to take it home – a commercial version of a thought-controlled bionic leg is still a long way from reality.

But one day, Huang hopes the results from the basic research in her lab will help people like Watson use their artificial leg just as they use their real one.

“With (this approach) an amputee should be able to just put on the prosthetic and go. We’re not there yet, but we’re making efforts to get there,” Huang said.

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