Touch Pressure Sensor Correction for Abnormal Readings
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Solution Overview
Problem
Current touch screen displays with damaged or broken touch pressure sensors provide inaccurate pressure or force data, leading to improper functionality due to the inability to effectively handle abnormal sensor operation.
Innovation Solution
A method and device that acquire touch pressure data from multiple sensors, determine abnormal operation by comparing data to limits, and apply correction factors to ensure accurate pressure output by summing and adjusting data from functioning sensors, either individually or by zone, during calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If touch pressure data from all sensors is summed directly, then the calculation is simple and fast, but the pressure output becomes inaccurate when abnormal sensors are present
Solution Approach 1:
The system performs preliminary identification of abnormal sensors by comparing each sensor's output against predetermined thresholds before summing the data. This preliminary action flags abnormal sensors so their data can be excluded from the final calculation, ensuring accurate pressure measurement while maintaining a relatively simple processing workflow.
Solution Approach 2:
The system extracts and removes data from sensors identified as abnormal through threshold comparison. By separating normal sensor data from abnormal sensor data and excluding the latter from the summation, the system ensures that only reliable measurements contribute to the final pressure output, resolving the accuracy issue without requiring complex correction algorithms.
2Reliability
If abnormal sensors are excluded from pressure calculation, then accurate pressure data is obtained, but the system becomes more complex requiring identification and correction mechanisms
Solution Approach 1:
The system implements a feedback mechanism where each sensor's output is continuously monitored and compared against predetermined thresholds. When a sensor's reading falls outside the acceptable range, the system provides feedback by flagging that sensor as abnormal and adjusting the pressure calculation accordingly. This automated feedback loop ensures reliable functionality while keeping the management complexity manageable through rule-based decision making.
Solution Approach 2:
The system performs self-diagnosis by automatically identifying which sensors are functioning abnormally through threshold comparison. Each sensor effectively monitors itself, and the system autonomously determines which sensor data to include or exclude from calculations without requiring external intervention or complex manual calibration, thereby maintaining reliability with minimal added complexity.
3Measurement precision
If correction factors are applied to compensate for abnormal sensors, then accurate pressure measurement is maintained, but the calibration process becomes more complex
Solution Approach 1:
The system changes the operational parameters of abnormal sensors by applying correction factors to their output data. Instead of physically replacing or recalibrating abnormal sensors, the system adjusts their numerical contributions to the pressure calculation through multiplicative correction factors, maintaining measurement accuracy while avoiding complex physical calibration procedures during manufacturing.
Solution Approach 2:
The system discards data from sensors that cannot be reliably corrected and recovers accurate pressure measurement by relying on the remaining normal sensors. When correction factors cannot adequately compensate for sensor abnormalities, the system excludes those sensor readings and reconstructs the pressure calculation using only valid data, thereby maintaining accuracy without requiring perfect calibration of all sensors.
Data Source
AI summary
A determination is made as to whether touch pressure data acquired from each of a plurality of touch pressure sensors is indicative of abnormal operation. If abnormal operation is indicated, the touch pressure data from each of the plurality of touch pressure sensors, except those touch pressure sensors having touch pressure data indicative of abnormal operation, is summed. Then, the sum is multiplied by a correction factor to produce a touch pressure output indicative of physical force applied to the plurality of touch pressure sensors.