Researchers at Stanford University have created a spray-on skin device that identifies objects just by touching them.
A team of researchers from Stanford University have developed a spray-on sensory system which consists of printed, bio-compatible nanomesh directly connected with a wireless bluetooth module and further trained through meta-learning. The device can identify objects by touch alone, or allow users to communicate by hand gestures with apps in immersive environments.
The device is a sprayable electrically sensitive mesh network embedded in polyurethane. The mesh is comprised of millions of nanowires of silver coated with gold that are in contact with each other to form dynamic electrical pathways. This mesh is electrically active, biocompatible, breathable, and stays on unless rubbed in soap and water. It conforms intimately to the wrinkles and folds of each human finger that wears it. Then a light-weight Bluetooth module can be simply attached to the mesh which can wirelessly transfer the signal changes.
The spray-on approach enables the device to function without any need of a substrate. This decision eliminated unwanted motion artifacts and allowed them to use a single trace of conductive mesh to generate multi-joint information of the fingers.
The device functions on the basis of a machine learning algorithm. Computers monitor the changing patterns in conductivity and map those changes to specific physical tasks and gestures. Type an X on a keyboard, for instance, and the algorithm learns to recognize that task from the changing patterns in the electrical conductivity. Eventually there will be no need for a physical keyboard. The same principles can be used to recognize sign language or even to recognize objects by tracing their exterior surfaces.
To meet this intense computational challenge and the need to compute vast amounts of data, the Stanford team has developed a learning scheme that is far more computationally efficient. This technology possesses a wide range of applications, for example it can enable new approaches to computer animation or lead to new avatar-led virtual meetings with more realistic facial expressions and hand gestures.
Reference : Kyun Kyu Kim et al, A substrate-less nanomesh receptor with meta-learning for rapid hand task recognition, Nature Electronics (2022). DOI: 10.1038/s41928-022-00888-7
