Prosthetic arms uses AI for more accurate and realistic movement

Prosthetic company Atom Limbs has combined advanced sensors and machine learning with prosthetics for a futuristic experience.

Prosthetics can often be limited by the number of nerves and muscles that amputees have; meaning movement could be checked depending on the individual’s situation. However, Atom Limbs might have found a forward-thinking solution, using sensors and AI machine learning to ‘train’ a prosthetic limb to become more accurate.

How does the AI-powered prosthetic work?

The limbs can interpret electrical signals from the wearer’s brain and use them to move and manipulate itself, just like the electrical pulses that govern our bodies. That means the prosthetic arms coming out of the company have a full range of human motion across the elbow, wrist, and even individual fingers. They also provide haptic feedback to the wearer to give insight into their grip strength, allowing them to easily hold delicate objects.

The arm can be attached using a strengthened sportswear-style vest, meaning it’s non-invasive and distributes the arm’s weight evenly for greater comfort. The fact that the prosthetic doesn’t need any surgery or implants to function is a major advantage over other prosthetics on the market at the moment.

Not only does the arm uses sensors to track instructions from the brain, but it also measures electrical signals across the limb via patches and a cup that fits over the top of the arm.

For all that, you can expect to pay something in the region of $20,000. While that might sound like a lot, it’s far less than many other bionic products on the market, improving accessibility to such high-value products. However, being able to purchase from Atom Limbs is still a way off, with the company’s prosthetics still under development. Data is being collected ahead of US regulatory processes, meaning a market-ready product isn’t coming any time soon.

Featured image: Atom Limbs

Rachael Davis

Freelance Journalist

Rachael Davies
has spent six years reporting on tech and entertainment, writing for publications like the Evening Standard, Huffington Post, Dazed, and more. From niche topics like the latest gaming mods to consumer-faced guides on the latest tech, she puts her MA in Convergent Journalism to work, following avenues guided by a variety of interests. As well as writing, she also has experience in editing as the UK Editor of The Mary Sue , as well as speaking on the important of SEO in journalism at the Student Press Association National Conference. You can find her full portfolio over on Muck Rack or follow her on social media on X.