Touch Detection Module Dual Filtering Noise Immunity
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Solution Overview
Problem
Capacitive touch sensor devices face significant disturbances from electromagnetic noise, leading to false 'not touch detected' readings and increased vulnerability to noise fluctuations, which reduces their ability to accurately detect touches.
Innovation Solution
A touch detection module that employs dual filtering techniques, using a 'fast' and 'slow' transfer function to create first and second filtered data signals, and calculates a delta value by subtracting the second filtered signal from the first, followed by integration and threshold comparison to enhance noise immunity and sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If filtering is applied to the sampled data to minimize electromagnetic noise, then noise immunity is improved, but sensitivity of the touch sensing components is reduced
Solution Approach 1:
The patent divides the touch sensing signal processing into multiple stages: initial sampling, first filtering stage, second filtering stage, and delta calculation. Each stage processes the signal with appropriate filtering intensity, segmenting the overall filtering process to balance noise reduction and sensitivity preservation
Solution Approach 2:
The patent dynamically adjusts filtering parameters based on the detected touch state. When no touch is detected, stronger filtering is applied to reduce noise. When a touch is detected, filtering is reduced or suspended to maintain sensitivity and accurate touch characterization
2Adaptability or versatility
If the baseline value is dynamically updated to track sampled data during touch events, then adaptability to ambient conditions is improved, but false 'not touch detected' readings occur due to noise fluctuations
Solution Approach 1:
The baseline value update mechanism is made dynamic and conditional. The system continuously monitors the delta value and only updates the baseline when confidence in touch detection is high (delta exceeds threshold). This dynamic adaptation prevents baseline tracking during noisy periods while maintaining adaptability during stable conditions
Solution Approach 2:
The system uses feedback from the delta calculation and threshold comparison to control baseline updates. When the delta value indicates a reliable touch detection, feedback triggers baseline updating. When noise causes false readings, the feedback mechanism prevents inappropriate baseline changes, ensuring reliable operation
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 provides greater noise immunity and higher sensitivity for touch recognition, allowing for more precise threshold settings and improved detection accuracy by continually processing sensor signals during touch and release conditions.
Implementation Method 1
capacitive touch sensor device includes one or more touch sensors, each of which is configured to indicate a capacitance change when, for example, the sensor is touched
Data Source
AI summary
A method and apparatus for performing touch detection within a touch sensing application is described. Touch sensor signal data is received, a first filtering of the received touch sensor signal data to create a first filtered data signal is performed, a second filtering of the received touch sensor signal data to create a second filtered data signal is also performed, a difference between the first and second filtered data signals to determine a delta value is calculated, and an occurrence of a touch based at least partly on the determined delta value is determined.