New bionic hand uses camera to 'see,' adjust grip

UK developers says technology enables hand to respond automatically to certain objects.
By Bernie Monegain
01:41 PM

Prosthetic limbs have changed very little in the past 100 years, according to doctors familiar with prosthetics. But a new bionic hand developed at Newcastle University in the United Kingdom has the potential to change that.

The bionic hand is fitted with a camera which instantaneously takes a picture of the object in front of it, assesses its shape and size and triggers a series of movements in the hand.

The project, described in the Journal of Neural Engineering this week, is led by biomedical engineers at Newcastle University and it is funded by the Engineering and Physical Sciences Research Council.

"Using computer vision, we have developed a bionic hand which can respond automatically – in fact, just like a real hand,” Kianoush Nazarpour, a senior lecturer in Biomedical Engineering at Newcastle University, said in a statement. “The user can reach out and pick up a cup or a biscuit with nothing more than a quick glance in the right direction,” he said. “Responsiveness has been one of the main barriers to artificial limbs. For many amputees, the reference point is their healthy arm or leg so prosthetics seem slow and cumbersome in comparison.”

The 'intuitive' hand can react without thinking. Today’s prosthetic hands are controlled via myoelectric signals – electrical activity of the muscles recorded from the skin surface of the stump.
The study’s lead author Ghazal Ghazaei showed the computer numerous object images and taught it to recognize the ‘grip’ that would work with different objects.

“We would show the computer a picture of, for example, a stick,” Ghazaei said in a statement. “But not just one picture, many images of the same stick from different angles and orientations, even in different light and against different backgrounds and eventually the computer learns what grasp it needs to pick that stick up.” 

So the computer isn’t just matching an image, it’s learning to recognize objects and group them according to the grasp type the hand has to perform to successfully pick it up.

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