Innovative prosthetic helps Army amputee compensate for loss of arm
WASHINGTON -- Retired Sgt. 1st Class Glen Lehman lost his right arm in 2008 during combat in Iraq.
Surgeries and rehabilitation followed at what was then known as Walter Reed Army Medical Center here, where he learned to use a prosthesis.
But the process of rehabilitation and learning to use the prosthesis took many months and was painful, he said, speaking Oct. 9, during a seminar on neural-controlled prosthetics at the Association of the United States Army Annual Meeting and Exposition.
The level of pain was such that it was "like doing eight hours straight of CrossFit training," said Lehman. "It was exhausting."
Besides that, once the device was mastered, it still didn't offer full functionality, he said. Instead of moving the elbow, wrist and hand simultaneously, for instance, each of those movements had to be done independently, in sequence. Using the device was also extremely uncomfortable, he said.
Then in 2013, U.S. Army Medical Research and Materiel Command, or MRMC, assisted by private industry, came up with a new upper-limb prosthetic that Lehman said he received.
It took just several weeks to learn, isn't painful to use, and mimics the full use of his arm, he said.
Instead of being "dependent" on his prosthetic, he said it now makes him feel completely "independent."
HOW IT WORKS
The new prosthetic arm is controlled by an array of electrodes that are placed in contact where the device meets the skin in a quick, easy, non-invasive manner, said Blair Lock, chief executive officer of COAPT LLC, the company that worked with MRMC on the design.
Electrodes are used because muscle contractions produce what are termed myoelectric signals, he said. Those signals are electrical impulses directed from the brain that contract muscle fibers.
Each person produces unique, but repeatable myoelectrical patterns for every given movement, he continued. After a series of repetitive motions, the sensors sense those patterns and algorithms in the software then instruct the prosthetic to move in those motions.
For example, he said the pattern of myoelectric activity recorded on the forearm during hand opening is different than the pattern recorded while the hand is being closed. "For those with upper limb loss those remaining muscles can produce these signal patterns even though a hand, wrist, or elbow isn't present," he said.
Over time, the algorithms learn these patterns, using artificial intelligence and in effect, a robot has been created, he added.
Lehman said that the prosthetic continues to learn and adapt with changes in a person's myoelectrical patterns.
The secret to all this is "intuitive control," Lehman said, explaining that "the arm has learned from me how I talk to the arm. As long as you can replicate the pattern, in just 30 minutes you can master that pattern."
He concluded: "Now I can open my hand, close the hand, pronate my wrist, supinate my wrist with ease, something I couldn't previously do."
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