Researchers have come a long way toward developing prosthetic limbs that give patients much of the same functionality that they had before they suffered a loss. Now, a team of researchers at two North Carolina universities has developed new technology to make prosthetic wrists and hands function even more intuitively based on the natural neuromuscular communication that controls normal human limbs.
Scientists in the joint biomedical engineering program at North Carolina State University and the University of North Carolina at Chapel Hill have invented technology that can decode neuromuscular signals to control powered, prosthetic wrists and hands.
This new method differs from the current state-of-the art in prosthetics, which relies on machine learning to create a pattern recognition that controls prosthetics, said He Huang, a professor in the program who led the research. In addition to providing more intuitive, realistic movements for prosthetic hands and wrists, she noted that the technology could be used to develop new computer interface devices for applications such as gaming and computer-aided design.
With even the most sophisticated prosthetics available today, users of the devices have to “teach” them to recognize specific patterns of muscle activity. The devices then translate these patterns into commands—for example, opening or closing a prosthetic hand—to control the devices, she said.
“Pattern-recognition control requires patients to go through a lengthy process of training their prosthesis,” Huang noted. “This process can be both tedious and time-consuming.”
Scientists in the joint biomedical engineering program at North Carolina State University and the University of North Carolina at Chapel Hill have invented technology that can decode neuromuscular signals to control powered, prosthetic wrists and hands, making them easier to use. (Image source: North Carolina State University)
Huang and the team set out to use their knowledge of the human body to make the process of controlling prosthetics more intuitive, as well as reliable and practical. Their technology—which relies on computer models that closely mimic the behavior of the natural structures in the forearm, wrist, and hand—does just that.
The team invented a virtual musculoskeletal model and sensors to receive the signals from the human brain to move a limb. Those signals still exist even if a person loses that part of the body, researchers said. They then send the data from those sensors to a computer, where it’s fed into the model that the team developed.
“The model takes the place of the muscles, joints, and bones, calculating the movements that would take place if the hand and wrist were still whole,” Huang explained. “It then conveys that data to the prosthetic wrist and hand, which perform the relevant movements in a coordinated way and in real time—more closely resembling fluid, natural motion.”
To develop their technology, the researchers placed electromyography sensors on the forearms of six able-bodied volunteers, tracking exactly which neuromuscular signals were sent when they performed various actions with their wrists and hands. This is how they created their generic model, which translated those neuromuscular signals into commands that manipulate a powered prosthetic, Huang said.
Researchers tested the device with both volunteers with amputations and those without missing limbs. Both could easily use the model-controlled interface to perform all of the required hand and wrist motions with very little training.
The team is now seeking people with transradial amputations to help them perform further testing of the model to see how it performs daily-living activities, Huang said. “We want to get additional feedback from users before moving ahead with clinical trials.”
The team published a paper on their work in the journal IEEE Transactions on Neural Systems and Rehabilitation Engineering. While the technology is still years away from commercial availability, the team is optimistic for its eventual success in use not only in prosthetics, but for future research into human-machine interfaces, Huang added.
Elizabeth Montalbano is a freelance writer who has written about technology and culture for 20 years. She has lived and worked as a professional journalist in Phoenix, San Francisco, and New York City. In her free time, she enjoys surfing, traveling, music, yoga, and cooking. She currently resides in a village on the southwest coast of Portugal.