Gesture Detection Using Signal Edge Extraction
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Solution Overview
Problem
Current touch-sensitive devices require complex and costly signal processing to determine gesture events, which are sensitive to environmental variations and device handling, and often involve the use of lookup tables for precise touch location analysis.
Innovation Solution
A method that simplifies gesture detection by using only the rising and falling edges of raw signals from transducers, such as piezoelectric or acoustic sensors, to identify touch events without the need for precise location determination, thereby reducing the complexity and cost of signal processing and making the system less sensitive to environmental and handling-related noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If precise touch location analysis is performed using powerful signal processing, then gesture detection accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts only the essential features (rising edge and falling edge of the raw signal) needed for gesture detection, discarding the need for comprehensive precise touch location analysis. This extraction approach maintains sufficient detection accuracy while dramatically simplifying the signal processing requirements.
Solution Approach 2:
The patent segments the gesture detection process into distinct phases: detecting the rising edge when the signal exceeds a threshold, identifying the gesture type during the active period, and detecting the falling edge when the signal returns below the threshold. This segmentation enables accurate gesture classification without requiring continuous precise location tracking.
2Reliability
If lookup tables and additional algorithms are used to account for environmental variations, then measurement reliability is improved, but device complexity increases
Solution Approach 1:
The patent employs a self-adjusting threshold mechanism that automatically adapts to environmental variations such as temperature and humidity changes. Instead of requiring external calibration data or complex compensation algorithms, the system services itself by dynamically adjusting its detection parameters based on real-time signal characteristics.
Solution Approach 2:
The patent changes the detection parameter from fixed threshold values to dynamic thresholds that adapt to environmental conditions. By monitoring the raw signal characteristics and adjusting the threshold accordingly, the system maintains reliable gesture detection across varying environmental conditions without requiring lookup tables or complex environmental compensation algorithms.
3Measurement precision
If acoustic pulse recognition with lookup tables is implemented, then touch location precision is improved, but manufacturing cost increases
Solution Approach 1:
The patent replaces expensive, complex signal processing systems with a simpler, more cost-effective approach that uses basic threshold comparison and edge detection. This simplified methodology achieves sufficient gesture detection accuracy without requiring costly lookup tables, precision calibration hardware, or complex processing algorithms, thereby reducing manufacturing costs.
Solution Approach 2:
The patent extracts only the critical information needed for gesture detection (rising and falling edges of the signal) rather than attempting to capture and process complete touch location data. This extraction eliminates the need for expensive lookup tables and complex acoustic pulse recognition systems while maintaining adequate detection capability for the intended application.
4Measurement precision
If complete gesture analysis with coordinate determination is performed, then gesture recognition accuracy is improved, but processing time increases
Solution Approach 1:
The patent skips the time-consuming steps of precise coordinate determination and comprehensive gesture analysis by directly detecting the rising and falling edges of the raw signal. This approach rushes through the detection process by focusing only on the essential temporal characteristics of the gesture, achieving adequate recognition accuracy without the processing overhead of detailed coordinate analysis.
Solution Approach 2:
The patent extracts only the temporal features (rising and falling edges) from the gesture signal, discarding the spatial coordinate information that would require extensive processing. This extraction enables rapid gesture detection by processing only the most critical temporal characteristics, significantly reducing processing time while maintaining sufficient recognition accuracy for the application.
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
This approach allows for reliable discrimination between real and unintended touch events, reducing the need for additional algorithms and lookup tables, and improves the robustness of gesture detection against temperature, humidity, and mechanical noise, while maintaining the ability to classify gestures accurately without precise coordinate determination.
Implementation Method 1
a touch location can be determined by comparing acoustic signals generated due to the user's touch interaction
Implementation Method 2
The at least one transducer can be, for example, a piezoelectric transducer, an acoustic sensor, an accelerometer, an optical sensor or any device having the functionality of transforming an input signal into an electric raw signal
Data Source
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AI summary
The present invention relates to a method for determining a gesture event in a touch sensitive device comprising at least one transducer, the method comprising the steps of: (a) sensing frames of a raw signal from the at least one transducer; (b) determining a value of a predetermined parameter for each frame of the raw signal; and (c) determining a rising edge and a falling edge in the raw signal based on the values of the predetermined parameter, thereby determining the gesture event.