May 4, 2016

Canadian Researchers Build a More Intuitive Bionic Hand

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From left, lead researcher Mehri and Menon are leading a team working with Letain on building a better bionic hand. The team tested the prototype at the Cybathlon trial held in July 2015 and is working to optimize the system for the competition. Photograph by Dale Northey, courtesy of SFU.

Researchers at Simon Fraser University (SFU), Burnaby, Canada, are working to design a new control system for Steeper Prosthetics’ bebionic3 myoelectric hand, with the goal of providing the user with a more intuitive experience. Para-athlete Danny Letain will put the technology to the test at the inaugural Cybathlon, an international competition for people with disabilities using robotic technology, in Zurich, Switzerland, on October 8.

Letain lost his left arm below the elbow 35 years ago. He has since used a body-powered prosthesis with a pincer-like split hook. “The hook is durable and quick to respond, but controlling it with straps is not natural,” Letain said.

With the SFU team’s new control system, Letain already has a variety of different grip patterns that he said work “well beyond” what he could achieve with other prosthetic devices. “With this new system, it feels like I’m opening and closing my hand. The most exciting moment for me was feeling my left index finger and the little finger for the first time since my accident,” he added. “With the hook you don’t use those muscles at all. This system puts my mind to work in a whole new way.”

The technology was developed in the biomedical engineering lab of Carlo Menon, PhD, an associate professor in the SFU School of Engineering Science, to rehabilitate stroke patients. He saw the potential for wider applications, including for individuals with upper-limb amputations.

The system consists of an armband of pressure sensors embedded in the prosthetic socket. The pressure sensors detect intricate muscle movements across the surface of the remaining limb as the user performs intuitive actions, such as grasping a bottle. Computer algorithms then map the sensor data to decode the user’s intentions and move the prosthesis. Besides being a more intuitive experience, it does not require the user to undergo extensive training to perform simple functions.

“The more data you give it, the more it will learn,” said SFU engineering science alumnus Lukas-Karim Merhi, MASc, who is leading the interdisciplinary team. The system takes incoming data and makes predictions in real time. It also generates computer models to improve future performance. Menon said he believes the technology could be used in combination with electromyography to become the standard for prosthetics.

Editor’s note: This story was adapted from materials provided by Simon Fraser University.

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