Sensor-packed glove learns signatures of the human grasp: Signals help neural network identify objects by touch; system could aid robotics and prosthetics design

The researchers developed a low-cost knitted glove, called "scalable tactile glove" (STAG), equipped with about 550 tiny sensors across nearly the entire hand. Each sensor captures pressure signals as humans interact with objects in various ways. A neural network processes the signals to "learn" a dataset of pressure-signal patterns related to specific objects. Then, the system uses that dataset to classify the objects and predict their weights by feel alone, with no visual input needed. In a paper published in Nature, the researchers describe a dataset they compiled using STAG for 26 common objects -- including a soda can, scissors, tennis ball, spoon, pen, and mug.