Touch Sensing Device Classifies Finger and Water Inputs
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Solution Overview
Problem
Touch display devices often malfunction due to unintended touch inputs from conductive materials like water or coins, which existing technologies fail to accurately distinguish from normal user inputs, leading to inaccurate touch sensing and potential device malfunctions.
Innovation Solution
A touch sensing device that employs a learning model-based decision tree to classify touch situations as normal or abnormal by analyzing the area, intensity, and uniformity of touch sensitivity, and adjusts its driving modes between mutual and self-sensing methods to improve input accuracy and prevent malfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional touch sensing methods are used, then all touch inputs are detected, but unintended inputs from conductive materials like water or coins cannot be distinguished from normal user inputs
Solution Approach 1:
The touch sensing method is segmented into multiple classification stages: first determining whether a touch object is a finger or non-finger based on capacitance characteristics, then further classifying non-finger touches into abnormal (conductive materials) or normal (non-conductive materials) categories. This multi-stage segmentation enables precise differentiation between various touch types that conventional single-stage methods cannot distinguish.
Solution Approach 2:
The patent utilizes changes in capacitance parameters (self-capacitance and mutual capacitance values) to differentiate between various touch objects. By analyzing the specific parameter ranges and patterns of capacitance changes, the system can identify whether a touch is from a finger, water, coin, or other materials, thereby improving both measurement precision and operational reliability.
2Ease of operation
If the touch sensing device classifies all touches as normal, then user inputs are always responsive, but unintended inputs from conductive materials cause malfunctions
Solution Approach 1:
The system implements feedback by continuously monitoring capacitance characteristics during touch events and automatically adjusting its response based on the classification results. When abnormal touches are detected, the system can provide feedback to the user (e.g., different haptic responses) or prevent erroneous operations, while maintaining normal responsiveness for genuine user inputs.
Solution Approach 2:
The touch sensing system dynamically adjusts its sensitivity and classification thresholds based on the detected touch characteristics. Rather than using fixed response rules, the system adapts its behavior in real-time according to the capacitance patterns observed, enabling it to maintain ease of operation for legitimate touches while preventing malfunctions from unintended inputs.
3Measurement precision
If the touch sensing device uses complex classification algorithms, then touch situation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The classification process is segmented into hierarchical stages with increasing complexity only when needed. The system first performs a quick initial classification to distinguish finger from non-finger touches using simple capacitance thresholds, then applies more complex analysis only to non-finger cases. This segmented approach maintains high accuracy while minimizing average processing time by avoiding unnecessary complex computations for simple finger touches.
Solution Approach 2:
The system applies partial classification action by using simplified classification rules for the majority of cases (finger touches) and reserving complex algorithms only for edge cases (non-finger touches). This partial application of complex algorithms reduces overall computational burden and processing time while maintaining high classification accuracy for all touch types.
Data Source
AI summary
A touch sensing device configured to drive a plurality of touch sensors according to one embodiment of the present disclosure includes a controller configured to calculate an area of a touch region, intensity of touch sensitivity, and uniformity of the touch sensitivity, and classify whether a touch situation is a normal touch situation or an abnormal touch situation based on the area of the touch region, and the intensity of the touch sensitivity, and the uniformity of the touch sensitivity using sensing signals input from the plurality of touch sensors.