Touch Point Detection Algorithm for Ghost Noise Elimination
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Solution Overview
Problem
Capacitive touch technologies often experience noise interference from factors like temperature and magnetic fields, leading to incorrect detection of untouched points as touch points, known as ghost points, which are difficult to eliminate completely due to the complexity of application scenarios and device environments.
Innovation Solution
A method and apparatus that determine a touch point by detecting touch signal values, identifying a center point with a report threshold, extracting and comparing signal values of surrounding points with auxiliary and abnormal thresholds to count points exceeding these thresholds, thereby distinguishing genuine touch points from ghost points caused by noise interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional touch point determination methods are used, then the touch detection function is provided, but ghost points are generated due to noise interference from temperature and magnetic fields
Solution Approach 1:
The patent segments the touch detection process into multiple stages: initial touch signal detection, auxiliary threshold comparison with surrounding points, and abnormal threshold validation. This multi-stage segmentation allows the system to distinguish genuine touch points from noise-induced ghost points by requiring multiple conditions to be met simultaneously, thereby improving reliability without adding complex hardware
Solution Approach 2:
The patent introduces auxiliary thresholds and abnormal thresholds as intermediary validation layers between the initial touch detection and final touch point confirmation. These intermediary thresholds act as filters that mediate between the raw touch signal and the final determination, preventing noise from directly causing false touch point identification while maintaining sensitivity to genuine touches
2Reliability
If multiple threshold comparisons and point counting are performed, then ghost points are reduced, but the processing complexity increases
Solution Approach 1:
The patent applies partial action by performing threshold comparisons only on a limited number of surrounding points (e.g., 4 adjacent points) rather than the entire touch screen. This partial sampling approach provides sufficient noise filtering while keeping the computational burden manageable. The abnormal threshold check is applied selectively based on the auxiliary threshold results, avoiding unnecessary full-screen processing
3Reliability
If the report point threshold is set high to reduce false positives, then ghost points decrease, but genuine touch points may be missed
Solution Approach 1:
The patent transitions from single-point threshold comparison to multi-dimensional validation by comparing the touch signal not only against the report point threshold but also against auxiliary thresholds of surrounding points and abnormal thresholds. This dimensional expansion from 1D (single threshold) to 2D/3D (multiple thresholds across multiple points) allows the system to maintain high sensitivity while reducing false positives through spatial context
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
This approach effectively reduces the occurrence of ghost points without increasing costs or processes, enhancing the accuracy of touch point detection and reducing the risk of adverse events by optimizing algorithm rules based on conventional touch point determination mechanisms.
Implementation Method 1
In the capacitive touch, the touch structure includes staggered electrodes, and fixed coupling capacitance exists between the electrodes. When being touched by fingers, the fixed coupling capacitance varies, so the position of the touch point can be determined by detecting such variation.
Data Source
AI summary
The present application provides a method of determining a touch point, including detecting a touch signal value of each point, determining a first point whose touch signal value is greater than a report point threshold as a center point, extracting touch signal values of a plurality of second points around the center point, comparing the extracted touch signal values of the plurality of second points with an auxiliary threshold, counting the number of the second points whose touch signal values are greater than the auxiliary threshold, and determining the first point as a touch point when the number of the second points whose touch signal values are greater than the auxiliary threshold is greater than or equal to a first preset value.