A new device 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. Reading hand gestures is one way of improving human-computer interaction. And while there are other ways of doing that, this solution also maintains an individual’s privacy.
To create the hand gesture recognition system, researchers 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 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.
When a person wants their hand muscles to contract, the brain sends electrical signals through neurons in the neck and shoulders to muscle fibers in the arms and hands. The electrodes in the cuff are sensing this electrical field to recognize certain patterns. 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.
The new device uses a type of AI called a hyperdimensional computing algorithm that is capable of updating itself with new information. 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. A process was developed so the learning is done on the device itself.