Touch Rejection for Wet Fabric via Path Classification
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Solution Overview
Problem
Touch-sensitive devices often incorrectly detect water or wet fabrics as intentional input, leading to unintended behavior and degraded performance in wet environments, as the electrical fields can mistakenly register these as touch inputs.
Innovation Solution
Implementing a classification system that distinguishes between touch paths and non-touch paths based on characteristics of edge touch nodes and determined states, allowing for the filtering out of non-touch paths corresponding to wet fabrics without requiring intensive processing algorithms, thereby improving touch performance and reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If touch sensor panels detect all contacts on the touch-sensitive surface, then touch input detection capability is improved, but false detection of water or wet fabric as intentional input occurs
Solution Approach 1:
The patent applies local quality by analyzing specific characteristics of touch contacts (such as contact area, pressure distribution, and temporal patterns) to distinguish between intentional finger touches and unintentional wet fabric contacts. Different regions and aspects of the touch signal are evaluated with different criteria to improve discrimination accuracy.
Solution Approach 2:
The system changes parameters by monitoring multiple touch signal parameters simultaneously (contact area, pressure, duration, movement patterns) and using their variations over time to differentiate between valid touches and false detections from wet materials. This multi-parameter approach enables reliable distinction between intentional and unintentional contacts.
2Measurement precision
If classification algorithms are used to distinguish touch paths from non-touch paths, then accuracy is improved, but processing requirements and power consumption increase
Solution Approach 1:
The patent implements partial action by applying classification only to paths that exhibit characteristics suggestive of potential false detections (such as contacts near edges or with ambiguous patterns). Not all touch paths undergo full classification processing, thereby reducing computational load while maintaining accuracy for problematic cases.
Solution Approach 2:
The system uses self-service by leveraging naturally occurring differences in touch signal characteristics that require minimal processing. The classification relies on inherent properties of the touch data (such as contact stability and movement patterns) that can be evaluated with simple algorithms, reducing the need for computationally intensive processing.
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 filters out unintended inputs from wet materials, enhancing the accuracy and efficiency of touch input processing in wet environments by classifying paths as either touch or non-touch, thus improving user experience and reducing processing requirements.
Implementation Method 1
In some capacitive-type touch sensing systems, fringing electrical fields used to detect touch can extend beyond the surface of the display, and objects approaching near the surface may be detected near the surface without actually touching the surface.
Data Source
AI summary
Touch input processing for touch-sensitive devices can be used to filter unintended contact detected on a touch-sensitive surface. Moist or wet fabrics on the edge of a touch-sensitive surface can be erroneously be detected as touch input and degrade touch performance. In some examples, input paths can be classified as touch paths or non-touch paths (corresponding to wet fabrics). Non-touch paths can be filtered out to avoid unintended input to a touch-sensitive device. Classifying paths can improve touch performance in environments where a wet fabric may come in contact with the edge of the touch-sensitive surface. In some examples, paths can be classified as touch paths or non-touch paths based on characteristics of edge touch nodes. In some examples, paths can be classified as touch paths or non-touch paths based on a determined state.