Capacitive Edge Sensing for Squeeze Gesture Detection
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Solution Overview
Problem
Existing touchscreen devices lack capabilities for interacting with edges beyond the main screen, limiting user interaction options.
Innovation Solution
Utilizing capacitive sensors to detect user interactions with the edges of touchscreen devices through methods such as strain gauges and machine learning algorithms like CNNs and RNNs to process capacitive data, enabling detection of gestures like squeezing and un-squeezing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional capacitive sensors are used for main screen interactions, then basic touch functionality is achieved, but edge interaction capabilities are limited
Solution Approach 1:
The existing capacitive sensor grid is made multi-functional by extending its detection capability from only the main screen area to include edge regions. The same sensor hardware detects both traditional touch interactions on the display and squeeze gestures on the edges, eliminating the need for separate strain gauge sensors while enabling diverse interaction modes including pinch, slide, and tap gestures on the device edges
2Measurement precision
If strain gauges are added to detect edge squeezing, then force measurement capability is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The mechanical strain gauge system is replaced with an electrical field-based detection system using the existing capacitive sensor grid. Instead of using mechanical deformation sensors, the system detects edge squeeze gestures by measuring changes in capacitive values at edge-adjacent sensor cells, thereby achieving force detection capability without adding mechanical components
Solution Approach 2:
The detection methodology from the main screen capacitive sensor is copied and applied to the edge regions. By treating edge-adjacent sensor cells similarly to how central display cells are processed, the system repurposes the existing sensor infrastructure to detect edge interactions, avoiding the need for separate dedicated edge sensing hardware
3Adaptability or versatility
If capacitive data from edge regions is processed, then edge gesture detection is enabled, but false triggers from normal holding increase
Solution Approach 1:
The system uses feedback from multiple sequential capacitive readings to distinguish intentional gestures from normal holding. By analyzing temporal patterns and requiring specific sequences of capacitive value changes at edge-adjacent cells, the system confirms whether a detected edge interaction represents a deliberate gesture or merely the user's natural grip on the device
Solution Approach 2:
The system processes capacitive data from a selective subset of sensor cells located adjacent to device edges, rather than analyzing the entire sensor grid. This focused processing of only relevant edge-adjacent cells reduces computational overhead and enables more sophisticated analysis of capacitive value patterns to improve gesture detection accuracy while minimizing false triggers
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
Enables additional functionalities by allowing user interactions with device edges, adapting to user preferences, and reducing action mis-triggers through calibration and sensor integration.
Implementation Method 1
capacitive sensor under the display... Each element in this 2D array corresponds to the capacitive value of a cell in the sensor
Data Source
AI summary
Methods and devices for detecting a user interaction with an at least one edge of the device are disclosed. The device has a touchscreen with a capacitive sensor and a processor. The method includes acquiring a sequence of input frames from the capacitive sensor, a given input frame comprising information about capacitive readings of the capacitive sensor along an at least one edge of the touchscreen, and continuously detecting the user interaction with the at least one edge of the electronic device using the sequence of input frames.