Elastic Wave Touch Sensing Gesture Recognition
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Solution Overview
Problem
Current touch sensing technologies face challenges in accurately recognizing user interactions, such as tapping and sliding gestures, due to limitations in distinguishing between synchronous and asynchronous elastic wave signals, which affects the accuracy and reliability of human-computer interaction.
Innovation Solution
A touch sensing method utilizing elastic wave sensors to acquire and analyze elastic wave signals, determining features like synchronous, asynchronous, or preset tap waveform features to differentiate between various touch sensing gestures, thereby improving the accuracy of gesture recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional touch sensing methods are used, then the system is simple to implement, but the accuracy of gesture recognition is insufficient
Solution Approach 1:
The patent segments the elastic wave signal analysis into distinct synchronous and asynchronous feature detection components. By dividing the signal processing into separate feature extraction modules (synchronous feature detection, asynchronous feature detection, and preset waveform matching), the system achieves high gesture recognition accuracy while maintaining manageable system complexity through modular design
Solution Approach 2:
The patent implements dynamic gesture recognition by detecting temporal relationships between elastic wave signals from multiple sensors. The system dynamically adapts to different gesture types (tapping, sliding, dragging) by analyzing the time-synchronization characteristics of signals, allowing accurate recognition without requiring complex predefined gesture templates for each scenario
2Reliability
If multiple elastic wave sensors are used to improve gesture differentiation, then the gesture recognition accuracy improves, but the device complexity increases
Solution Approach 1:
The patent applies partial action by focusing detection resources on key discriminative features rather than analyzing all possible signal characteristics. The system selectively extracts synchronous and asynchronous features that are most critical for gesture differentiation, achieving reliable gesture recognition without the need for complex analysis of every signal parameter from multiple sensors
Solution Approach 2:
The patent changes the analysis parameter from raw signal amplitude to temporal synchronization characteristics. By transforming the detection focus to time-based features (whether signals occur simultaneously or sequentially), the system achieves high gesture differentiation accuracy using simple comparative logic rather than complex multi-parameter analysis
3Measurement precision
If synchronous and asynchronous feature detection is implemented, then the touch sensing accuracy improves, but the signal processing complexity increases
Solution Approach 1:
The patent segments signal processing into three independent feature detection pathways: synchronous feature detection, asynchronous feature detection, and preset waveform matching. Each pathway processes specific aspects of the elastic wave signals independently, then results are combined for final gesture determination. This segmentation achieves high touch sensing accuracy while keeping individual processing modules simple and manageable
Solution Approach 2:
The patent inverts the traditional approach by not trying to classify gestures directly from raw signals, but instead by detecting what is absent or present in terms of synchronization patterns. The system determines gestures by identifying whether signals are synchronous or asynchronous, and matching against preset waveforms, which simplifies the processing logic compared to direct classification methods
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
Enhances the accuracy of touch sensing by effectively distinguishing between different gestures, leading to improved human-computer interaction experiences by accurately recognizing tapping, sliding, and other touch events.
Implementation Method 1
acquiring an elastic wave signal detected by at least one elastic wave sensor mounted on the touch sensing apparatus
Data Source
AI summary
Disclosed are a touch sensing method and apparatus, applied to a touch sensing apparatus including at least one elastic wave sensor, the method includes: acquiring an elastic wave signal detected by at least one elastic wave sensor mounted on the touch sensing apparatus; determining a feature of the elastic wave signal, including: a synchronous feature, an asynchronous feature, or a preset tap waveform feature; and performing touch sensing according to the feature of the clastic wave signal.