Capacitance Module Calibration for Unprompted Palm Detection
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Solution Overview
Problem
Existing capacitive touch devices struggle with unintentional activation due to accidental touches from the palm, leading to frustrating unintended actions and inaccurate differentiation between intentional and accidental inputs.
Innovation Solution
A capacitance module with electrodes and a controller that performs calibration by prompting finger and palm inputs, storing attributes, and determining unprompted palm inputs through machine learning models, using dimension, movement, and signal attributes to distinguish between intentional and accidental touches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing palm rejection methods (ignoring large touch areas or edge inputs) are used, then the frequency of accidental touches is reduced, but the accuracy of differentiating between intentional and accidental touches deteriorates
Solution Approach 1:
The patent segments the touch analysis into multiple independent attribute dimensions: touch area, touch duration, touch pressure, touch location, and movement patterns. Each attribute is evaluated separately to determine whether a touch is intentional or accidental, allowing for more precise differentiation than simple area-based rejection methods.
Solution Approach 2:
The patent changes from using single-parameter rejection (touch area size) to multi-parameter analysis (area, duration, pressure, location, movement). By considering multiple parameters simultaneously, the system achieves both reliable palm rejection and accurate touch differentiation, resolving the contradiction between these two objectives.
2Device complexity
If simple rejection rules (ignoring edge contacts or large areas) are applied, then device complexity is reduced, but touch input accuracy deteriorates
Solution Approach 1:
The system performs self-calibration by automatically learning user-specific touch patterns during initial setup and ongoing use. The device adapts to individual user behaviors without requiring manual configuration, achieving high accuracy while maintaining relatively simple operational complexity for the user.
Solution Approach 2:
The patent implements preliminary calibration processes where the system learns user touch patterns before normal operation begins. This pre-learning phase establishes baseline characteristics for intentional touches, enabling more accurate real-time differentiation without adding complexity to the main operational flow.
3Measurement precision
If calibration processes storing multiple attributes (dimension, movement, signal) are implemented, then touch input accuracy is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple attribute measurements (dimension, movement, signal characteristics) into a unified touch analysis framework. By merging these attributes into a comprehensive evaluation model, the system achieves high measurement precision while managing complexity through integrated processing rather than separate analysis of each attribute.
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
Enhances touch input accuracy by effectively distinguishing between intentional and accidental inputs, reducing false activations and improving user experience.
Implementation Method 1
A touch pad is often incorporated into laptops and other devices to provide a mechanism for giving inputs to the device. One issue with capacitive touch input devices is the unintentional activation caused by accidental touches
Data Source
AI summary
A capacitance module may include a set of electrodes, a controller in communication with the set of electrodes, and memory in communication with the controller. The memory may include programmed instructions that cause the capacitance module, when executed, to perform a calibration, the calibration including prompting a finger input, storing a finger attribute of a finger capacitance measurement associated with the finger input, prompting a palm input, storing a palm attribute of a palm capacitance measurement associated with the palm input, and determining an unprompted palm input by consulting at least one of the stored finger attribute and the stored palm attribute.