UC Berkeley researchers have created a new device that combines wearable biosensors with artificial intelligence software to help recognize what hand gesture a person intends to make based on electrical signal patterns in the forearm. The device paves the way for better prosthetic control and seamless interaction with electronic devices.
Imagine typing on a computer without a keyboard, playing a video game without a controller or driving a car without a wheel.
That’s one of the goals of a new device developed by engineers at the University of California, Berkeley, that can recognize hand gestures based on electrical signals detected in the forearm. The system, which couples wearable biosensors with artificial intelligence (AI), could one day be used to control prosthetics or to interact with almost any type of electronic device.
Prosthetics are one important application of this technology, but besides that, it also offers a very intuitive way of communicating with computers.
Reading hand gestures is one way of improving human-computer interaction. And, while there are other ways of doing that, by, for instance, using cameras and computer vision, this is a good solution that also maintains an individual’s privacy.
The team has designed a flexible armband that can read the electrical signals at 64 different points on the forearm. The electrical signals are then fed into an electrical chip, which is programmed with an AI algorithm capable of associating these signal patterns in the forearm with specific hand gestures.
The team succeeded in teaching the algorithm to recognize 21 individual hand gestures, including a thumbs-up, a fist, a flat hand, holding up individual fingers and counting numbers.
Like other AI software, the algorithm has to first “learn” how electrical signals in the arm correspond with individual hand gestures. To do this, each user has to wear the cuff while making the hand gestures one by one.
However, the new device uses a type of advanced AI called a hyperdimensional computing algorithm, which is capable of updating itself with new information.
For instance, if the electrical signals associated with a specific hand gesture change because a user’s arm gets sweaty, or they raise their arm above their head, the algorithm can incorporate this new information into its model.
Another advantage of the new device is that all of the computing occurs locally on the chip: No personal data are transmitted to a nearby computer or device. Not only does this speed up the computing time, but it also ensures that personal biological data remain private.
News Source: UC Berkeley
