Touch Panel Tap Classification Using Vibration Signals
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Solution Overview
Problem
Existing large-sized touch display devices face inconvenience in accurately identifying tap events due to the distance of the main menu from the user, making it difficult to adjust brush properties effectively.
Innovation Solution
A method and system utilizing a deep neural network trained with vibration signals and touch sensing values to predict tap types, improving accuracy by using a vibration sensor and touch panel to detect tap events, transform sensing values into images, and fine-tune weighting parameters through a backpropagation algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of moving object
If a large-sized touch display device is used to provide a bigger display area, then the display area is increased, but the distance between the main menu and the user increases, making it inconvenient to click and adjust brush properties
Solution Approach 1:
The patent segments the touch panel into multiple functional zones: a first touch area for marking/drawing operations and a second touch area (edge area) for menu access and property adjustment. This segmentation allows users to access the main menu and adjust brush properties by tapping near the edge of the screen without needing to reach across the entire large display area, thus resolving the contradiction between large display size and ease of operation.
2Loss of information
If the main menu is placed on the edge of the screen to be always visible, then the menu is always visible, but the distance from the user increases, making it very inconvenient to click
Solution Approach 1:
The patent divides the touch panel into distinct functional zones where the edge area serves as a dedicated access zone for the main menu. This segmentation ensures the menu remains visible while being easily accessible through edge taps, resolving the contradiction between menu visibility and ease of access.
Solution Approach 2:
The patent introduces an intermediary mechanism where edge tap gestures serve as a mediator between the user and the main menu. Instead of requiring direct clicking on distant menu items, users can tap near the edge to access menu functions, making interaction more convenient while maintaining menu visibility.
3Measurement precision
If vibration signals and touch sensing values are used together to identify tap events, then the accuracy of tap type prediction is improved, but the device complexity increases
Solution Approach 1:
The patent combines vibration signal detection and touch sensing value detection into a unified tap event identification system. By merging these two detection mechanisms and processing their data together through a coordinated algorithm, the system achieves high-accuracy tap type prediction while avoiding the need for completely separate detection systems, thus balancing accuracy improvement with controlled device complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of predicting tap types on touch panels, allowing for more precise control of touch operations and improving user interaction with touch-controlled end products.
Implementation Method 1
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
Implementation Method 2
the touch panel is configured to detect each of the tap events to obtain a plurality of touch sensing values
Data Source
AI summary
A method for identifying tap events on a touch panel includes measuring vibration signals for tap events on the touch panel to collect the tap events and record types of the tap events as samples; generating a sample set including a plurality of samples; using the sample set to train a deep neural network to determine an optimized weighting parameter group; taking the deep neural network and the optimized weighting parameter group as a tapping classifier and deploying it to a touch-controlled end product; and obtaining a predicted tap type based on a vibration signal and an image formed by touch sensing values detected by the touch-controlled end product to which a tap operation is performed. The present disclosure also provides a system corresponding to the identifying method, and a touch-controlled end product.