Researchers build a better bionic hand

Researchers at Simon Fraser University are working with paralympic skier Danny Letain to design a new control system for one of the world’s most advanced bionic hands, promising a more intuitive experience for upper limb amputees.

Letain, a former locomotive engineer, lost his left arm below the elbow 35 years ago. He has since used a body-powered prosthesis with a pincer-like split hook that uses a series of straps to mechanically maneuver the artificial limb.



“The hook is durable and quick to respond, but controlling it with straps is not natural,” says Letain. Yet with the SFU team’s new control system, Letain already has a variety of different grip patterns that he says work “well beyond” what he could achieve with prosthetic devices.

Letain adds: “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. 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 engineering science professor Carlo Menon’s biomedical engineering lab initially to rehabilitate stroke patients. He immediately saw the potential for wider applications, however, including for amputees.

Menon says there is a high rejection rate among those with existing robotic prostheses because they are not intuitive.

“The problem is in the control systems, which have not significantly advanced in 50 years,” he says. “As a result, the robotic prostheses are not very useful for performing everyday tasks, and only about a quarter of amputees use them.”

The SFU team, known as M.A.S.S. Impact (Muscle Activity Sensor Strip), is applying its technology to a robotic arm on loan from Steeper Prosthetics, a company in Leeds, England. The SFU team has been working with Letain and staff at Vancouver’s Barber Prosthetics since June 2015.

The new system consists of an armband of pressure sensors embedded in the prosthetic socket. These track movements in Letain’s remaining muscles as he performs intuitive actions, such as grasping a bottle. Computer algorithms then map the sensor data to decode his intentions and move the prosthesis.

Full story can be found from Simon Fraser University website.