Force Sensor Pair Cross-Correlation for Squeeze Input Detection
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Solution Overview
Problem
Existing force sensor technologies face challenges in accurately and robustly detecting user squeeze inputs on portable devices, particularly in processing sensor signals to differentiate between various types of user interactions effectively.
Innovation Solution
A device equipped with a pair of force sensors that utilize cross-correlation between sensor signals to detect user squeeze inputs, employing smoothing parameters and normalization techniques to generate a cross-correlation value, which is then compared to a threshold to determine the presence of a squeeze input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mechanical switches or simple force sensors are used to detect user interactions, then the device structure is simple, but the ability to accurately differentiate between various types of user interactions is insufficient
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple interaction patterns (single-handed grip, two-handed grip, palm rest, etc.) and their expected sensor signal characteristics before actual use. The controller is pre-programmed with correlation thresholds and interaction type classifications, enabling it to quickly match real-time sensor data against these predefined patterns without requiring complex real-time analysis algorithms.
Solution Approach 2:
The system implements feedback by continuously monitoring sensor signals from multiple force sensors, comparing them against predefined interaction patterns, and dynamically determining the current interaction type. The cross-correlation operation provides feedback on how well the current sensor readings match expected patterns, allowing the system to adaptively identify and respond to different user interactions in real-time.
2Adaptability or versatility
If multiple force sensors are used to detect different user interactions, then the detection capability is improved, but the complexity of processing sensor signals to differentiate interactions increases
Solution Approach 1:
The patent applies segmentation by dividing the detection of user interactions into distinct, predefined categories (single-handed grip, two-handed grip, palm rest, etc.). Each interaction type is associated with specific patterns of sensor activation and force distribution. This segmentation allows the system to process complex multi-sensor data by matching it against discrete, manageable interaction patterns rather than attempting to analyze all possible interaction variations simultaneously.
Solution Approach 2:
The system implements universality by using a single signal processing approach (cross-correlation analysis) that can identify multiple different interaction types from the same set of force sensors. The same hardware configuration and processing algorithm universally handle various interactions including single-handed and two-handed gripping, palm resting, and different grip strengths, eliminating the need for separate detection mechanisms for each interaction type.
3Reliability
If cross-correlation analysis is applied to sensor signals to improve interaction detection, then the robustness of squeeze input detection is improved, but the computational processing requirements increase
Solution Approach 1:
The patent applies partial action by implementing cross-correlation analysis selectively rather than continuously. The system performs cross-correlation operations primarily during periods when squeeze inputs are detected or suspected, rather than constantly analyzing all sensor data. This partial application of the computationally intensive cross-correlation method reduces overall energy consumption while maintaining robust detection capability when needed.
Data Source
AI summary
A device, comprising: a pair of force sensors located for detecting a user squeeze input; and a controller operable in a squeeze detection operation to detect the user squeeze input based on a cross-correlation between respective sensor signals originating from the pair of force sensors.