Capacitive Touch Signal Filtering With Walsh-Hadamard Detection
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Solution Overview
Problem
Conventional capacitive touch systems face challenges in reliably detecting touch events by gloved fingers due to insufficient capacitance changes and are often overly sensitive to bare hands or prone to false detections from noise and contaminants, requiring tuning that balances sensitivity and specificity.
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, especially for gloved fingers, by evaluating transformed signals in sequency bins, which provides a more robust and efficient method for determining touch events.
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 is overly sensitive to noise and contaminants and produces false detections
Solution Approach 1:
The patent segments the raw capacitance signal into multiple frequency components using Fast Fourier Transform (FFT), dividing the signal spectrum into bins representing different frequency ranges. This segmentation allows selective filtering of noise frequencies while preserving touch event frequencies, thereby improving detection reliability without requiring complex hardware modifications.
Solution Approach 2:
The patent introduces an intermediary signal processing stage between the capacitive sensor and the touch detection logic. This intermediary layer applies FFT-based frequency domain analysis and filtering to transform the raw capacitance signal into a processed signal that better distinguishes genuine touch events from noise and contaminants, improving reliability while maintaining system architecture simplicity.
2Measurement precision
If the system increases sensitivity to detect touch by gloved fingers, then detection capability for gloved fingers improves, but the system becomes overly sensitive to bare hands and produces more false detections
Solution Approach 1:
The patent applies local quality by treating different frequency components of the capacitance signal differently. Through frequency bin analysis, the system identifies specific frequency ranges characteristic of genuine touch events versus noise and contaminants. This allows the system to enhance sensitivity for gloved finger detection in relevant frequency bands while suppressing false detections from other sources, achieving both goals simultaneously.
Solution Approach 2:
The patent changes the parameter domain from time-domain capacitance values to frequency-domain spectral components. By transforming the signal and analyzing specific frequency bins, the system can dynamically adjust detection thresholds for different frequency ranges, enabling enhanced sensitivity for gloved fingers while maintaining reliability by filtering out frequency components associated with false detections.
3Ease of operation
If conventional systems use time-averaged baseline capacitance for touch detection, then the detection method is simple, but the system cannot effectively distinguish between touch and proximity events
Solution Approach 1:
The patent transitions from one-dimensional time-domain baseline comparison to two-dimensional frequency-domain analysis. By applying FFT and examining spectral distribution across multiple frequency bins, the system gains an additional dimension for distinguishing touch events from proximity events. Genuine touches produce characteristic frequency signatures that differ from proximity effects, enabling accurate differentiation while maintaining computational efficiency.
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 signal-to-noise ratio and improves the system's ability to accurately detect touch events, including those by gloved fingers, while reducing false positives from noise and contaminants, thereby enhancing the reliability and sensitivity of capacitive touch systems.
Implementation Method 1
The control circuit provides an excitation voltage to, and thereby generates an electric field about, the sensor electrode
Implementation Method 2
Introduction of a stimulus, for example, a user's finger, to or near the sensor electrode or corresponding touch surface establishes an additional capacitance, sometimes referred to as a finger capacitance, from the electrode, through the finger, to ground
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.