Proximity Detection Thresholding for Background Drift and Touch Lock-Up
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Solution Overview
Problem
Conventional proximity detection systems face issues with system lock-up due to prolonged proximity and fail to differentiate between changes in background average levels, leading to missed detections and incorrect touch recognition.
Innovation Solution
A proximity detection system that adjusts the weighting in moving average calculations and uses a reset timer to differentiate between prolonged elevated and decreased background levels, allowing for gradual updates and preventing system lock-up, thereby enabling detection of subsequent touches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses a moving average to represent background conditions, then slowly varying environmental conditions can be detected, but the system locks out subsequent valid touches after a spurious touch indication
Solution Approach 1:
The patent applies dynamics by making the background average level adaptive rather than static. The system dynamically adjusts the background average based on detected proximity events, allowing it to evolve over time. This resolves the contradiction by enabling the system to maintain reliability through continuous adaptation while avoiding lockout, as the background level updates to reflect new environmental conditions rather than blocking subsequent detections
Solution Approach 2:
The patent implements feedback by using detected proximity events to update the background average level. The system monitors detection outcomes and feeds this information back into the background level calculation, creating a closed-loop system. This feedback mechanism allows the system to learn from spurious touches and adjust accordingly, maintaining both reliability and responsiveness by preventing future lockouts based on historical detection patterns
2Reliability
If the system resets after a predetermined time period, then it recovers from prolonged proximity, but it fails to differentiate between increased background average and missed touches
Solution Approach 1:
The patent applies parameter changes by modifying how the background average level is calculated and updated based on detection outcomes. Instead of using a fixed time-based reset, the system changes the background level parameter dynamically based on whether proximity events are detected. This allows the system to distinguish between genuine background shifts and missed touches by observing patterns in detection data, thereby improving measurement precision while maintaining reliable recovery
Solution Approach 2:
The system transitions from static time-based resetting to dynamic event-based background level adjustment. The background average evolves based on actual detection events rather than following a predetermined time schedule. This dynamic approach enables the system to adapt its recovery behavior to actual environmental conditions, improving both reliability and detection precision by responding to real events rather than arbitrary time intervals
3Stability of the object's composition
If the background average level remains fixed, then the system maintains stability, but it misses valid proximity events when background level decreases
Solution Approach 1:
The patent resolves this contradiction by making the background average level dynamic rather than fixed. The system continuously updates the background level based on detected proximity events, allowing it to adapt to decreasing background levels while maintaining stability through controlled evolution. This dynamic adjustment ensures the system remains stable overall while reliably detecting valid proximity events even when background conditions change
Solution Approach 2:
The system implements feedback by monitoring proximity detection outcomes and using this information to adjust the background average level. When valid proximity events are detected, the feedback mechanism triggers updates to the background level, ensuring it remains accurate反映 current environmental conditions. This feedback loop maintains stability while improving detection reliability, as the background level adapts to prevent missing events during background level decreases
Data Source
AI summary
Proximity detection is accomplished by determining with a moving average calculation a moving average level of input data; setting a threshold level in response to the average level and a sensitivity factor; producing a proximity detection output when the input data meets the threshold level; and changing the weighting used by the average level calculation in response to a proximity detection output.