Method and system for identifying tap events on touch panel, and touch-controlled end project

A method for identifying tap events on a touch panel, comprising:

• detecting various tap events on the touch panel to obtain a plurality of measured vibration signals, using the touch panel to detect each of the tap events to obtain a plurality of touch sensing values and transforming the touch sensing values into an image, and recording types of the tap events as classification labels;
• taking the vibration signal, the image, and the classification label that correspond to one of the tap events as a sample, and generating a sample set comprising a plurality of samples;
• taking the samples of the sample set as inputs and a freely-selected weighting parameter group as adjusting parameters and inputting them into a deep neural network for training the deep neural network by adjusting the weighting parameter group by way of a backpropagation algorithm;
• and
• taking out the samples of the sample set in batches to train the deep neural network and fine tune the weighting parameter group to determine an optimized weighting parameter group.

A system for identifying tap events on a touch panel, comprising:

• a touch panel;
• a vibration sensor, deployed with the touch panel, wherein the vibration sensor is configured to detect various tap events on the touch panel to obtain a plurality of measured vibration signals, and the touch panel is configured to detect each of the tap events to obtain a plurality of touch sensing values and the touch sensing values are transformed into an image;
• a processor coupled to the vibration sensor and the touch panel, configured to receive the vibration signals transmitted from the vibration sensor and a plurality of images transmitted from the touch panel;
• and
• a memory, connected to the processor, the memory comprising a plurality of program instructions executable by the processor, the processor executing the program instructions to perform a method comprising:
• recording types of the tap events as classification labels in the memory;
• taking the vibration signal, the image, and the classification label that correspond to one of the tap events as a sample, and generating a sample set comprising a plurality of samples;
• taking the samples of the sample set as inputs and a freely-selected weighting parameter group as adjusting parameters and inputting them into a deep neural network for training the deep neural network by adjusting the weighting parameter group by way of a backpropagation algorithm;
• and
• taking out the samples of the sample set in batches to train the deep neural network and fine tune the weighting parameter group to determine an optimized weighting parameter group.