Capacitive Touch Association Using Multi-Attribute Similarity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing touch-sensor devices face issues with accurately associating multiple detected touches, leading to false positive and false negative associations, which hinder proper user interface functionality.
Innovation Solution
A capacitive sensing system that calculates similarity values based on multiple touch attributes such as shape, position, velocity, and orientation to accurately associate detected touches, ensuring correct recognition of gestures and user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple detected touches are associated using existing techniques, then user interface functions can be initiated, but false positive and false negative associations occur reducing functionality
Solution Approach 1:
The patent transitions from simple touch detection to multi-dimensional touch characterization by analyzing attributes including shape, position, velocity, and orientation. This dimensional expansion enables more accurate touch association by comparing touches across multiple parameters rather than relying on single-point detection, thereby resolving false positive and false negative associations.
Solution Approach 2:
The system dynamically evaluates multiple touch parameters (shape, position, velocity, orientation) to determine whether detected touches should be associated. By changing from static touch detection to dynamic multi-parameter analysis, the system achieves more reliable touch association while maintaining manageable complexity through systematic parameter comparison.
2Measurement precision
If simple touch detection is used, then device operation is simple, but false associations occur reducing proper functionality
Solution Approach 1:
The patent performs preliminary analysis of touch attributes (shape, position, velocity, orientation) before making association decisions. By pre-characterizing each touch with multiple attributes, the system establishes a foundation for accurate association without requiring complex real-time processing during gesture recognition, thus maintaining ease of operation while improving precision.
Solution Approach 2:
The system replaces simple mechanical touch detection with a multi-attribute analysis framework that evaluates touches based on shape, position, velocity, and orientation parameters. This substitution transforms basic contact detection into precise gesture recognition while maintaining user-friendly operation through automated attribute comparison.
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 system effectively minimizes false associations by using multiple attributes to describe touches, allowing for accurate representation and interpretation of gestures and inputs in various user interface functions.
Implementation Method 1
Capacitive sensing typically involves measuring a change in capacitance associated with the capacitive sensor elements to determine a presence or position of a conductive object relative to a touch input device
Data Source
AI summary
A method and apparatus determine a plurality of attribute values of a first detected presence, determine another plurality of attribute values of a second detected presence, and associate the first detected presence with the second detected presence based on the plurality of attribute values and the other plurality of attribute values.