Method and apparatus for estimating touch locations and touch pressures

A method for acquiring tactile sensing data, the method comprising:

• controlling a first piezoelectric element to generate an acoustic wave, along a surface of an object;
• receiving, by using a second piezoelectric element, a signal corresponding to the generated acoustic wave;
• identifying a first variance of the received signal in case that the surface of the object is touched, and a second variance of the received signal in case that there is no touch on the surface of the object;
• selecting frequency bands from a plurality of frequency bands of the received signal, based on the first variance of the signal, and the second variance of the signal;
• executing, based on the received signal corresponding to the selected frequency bands, a neural network that is trained to provide touch prediction scores corresponding to a plurality of defined locations on the surface of the object;
• and
• identify a location of a touch input based on the touch prediction scores provided from the neural network.

A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor individually or collectively, cause the at least one processor to:

• control a first piezoelectric element to generate an acoustic wave, along a surface of an object;
• receive, by using a second piezoelectric element, a signal corresponding to the generated acoustic wave;
• identify a first variance of the signal that is received in case that the surface of the object is touched, and a second variance of the signal that is received in case that there is no touch on the surface of the object;
• select frequency bands from a plurality of frequency bands of the signal, based on the first variance of the signal and the second variance of the signal;
• execute a neural network based on the received signal corresponding to the selected frequency bands, the neural network trained to provide touch prediction scores corresponding to a plurality of defined locations on the surface of the object;
• and
• identify a location of a touch input based on the touch prediction scores provided from the neural network.