Capacitive Touch Signal Filtering for Gloved Finger Detection
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Solution Overview
Problem
Conventional capacitive touch systems face challenges in reliably detecting touch events by gloved fingers due to smaller changes in capacitance, often leading to false negatives or false positives, especially when distinguishing between bare and gloved fingers, and are susceptible to noise and contaminants.
Innovation Solution
Transforming raw capacitance signals from the time domain to the sequency domain using a Walsh-Hadamard transform allows for improved signal processing, enabling better differentiation between touch and proximity events by evaluating transformed signals in sequency bins, which provides a better signal-to-noise ratio and reduces computational complexity compared to Fourier transforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional capacitive touch systems use raw capacitance threshold comparison to detect touch events, then the system is simple to implement, but the system produces false positives and false negatives when distinguishing between bare and gloved fingers
Solution Approach 1:
The patent replaces conventional time-domain signal processing with a frequency-domain approach using Fast Fourier Transform (FFT). This substitution transforms the raw capacitance signal into frequency components, allowing the system to distinguish between different touch types (bare finger vs. gloved finger) by analyzing spectral characteristics rather than raw amplitude values, thereby improving detection accuracy while maintaining computational efficiency
Solution Approach 2:
The patent changes the parameter domain from time-domain raw capacitance values to frequency-domain spectral components. By computing the FFT of the capacitance signal and analyzing specific frequency bins, the system extracts features that are more discriminative for touch classification. This parameter transformation enables reliable distinction between bare and gloved finger touches by identifying characteristic frequency patterns associated with each touch type
2Measurement precision
If the system increases sensitivity to detect gloved finger touches, then detection capability improves, but false positives from noise and contaminants increase
Solution Approach 1:
The patent extracts specific frequency components from the capacitance signal using FFT analysis. By focusing on particular frequency bins that are characteristic of genuine touch events and filtering out other frequency ranges, the system isolates the useful signal from noise and contaminant interference. This extraction approach allows the system to maintain high sensitivity for detecting subtle capacitance changes from gloved fingers while rejecting false signals from environmental noise
Solution Approach 2:
The patent introduces frequency-domain analysis as an intermediary processing stage between raw capacitance measurement and touch event determination. The FFT transformation acts as a mediator that translates the capacitance signal into a representation where noise and genuine touches are separated by their spectral characteristics. This intermediary step enables the system to achieve high measurement precision for gloved finger detection while being robust against noise and contaminants
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
The Walsh-Hadamard transform enhances the system's ability to detect touch events by gloved fingers while minimizing false positives and negatives, improving sensitivity and specificity, and reducing the need for complex computations.
Implementation Method 1
The control circuit provides an excitation voltage to, and thereby generates an electric field about, the sensor electrode. This electric field establishes a capacitance, sometimes referred to as a parasitic capacitance, from the sensor electrode to ground or another reference potential
Implementation Method 2
The control circuit provides an excitation voltage to, and thereby generates an electric field about, the sensor electrode
Data Source
AI summary
A transform is used to transform raw sensor data from the time domain to the frequency or sequency domain. The transformed data falls into several signal bins. The transformed data in at least one of the signal bins is analyzed to determine whether a touch event or release event has occurred.