Adaptive Gesture Recalibration for Touch Input Accuracy
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Solution Overview
Problem
Existing touch-sensitive devices face challenges in accurately detecting finger gestures due to variations in user physical characteristics, skin textures, and habitual behaviors, leading to incorrect gesture interpretations and a poor user experience for a significant minority of users.
Innovation Solution
An adaptive recalibration process for gesture recognition is implemented, where the system automatically initiates recalibration based on user behavior, detecting changes in gesture patterns and switching to a more appropriate calibration profile when the existing one fails, using a combination of sensors like accelerometers and touchscreens to monitor and adjust gesture detection parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed calibration profile is used for gesture detection, then the system is simple to operate, but gesture recognition accuracy deteriorates for users with different physical characteristics and behaviors
Solution Approach 1:
The system dynamically switches between multiple calibration profiles based on detected gesture patterns. Instead of using a single static calibration, the system monitors user gestures and automatically selects the most appropriate calibration profile from a set of pre-defined profiles, allowing the detection parameters to adapt to different user characteristics without requiring manual re-calibration.
Solution Approach 2:
The system changes detection parameters by switching between different calibration profiles. Each profile contains optimized parameters for specific gesture detection scenarios, and the system selects the appropriate profile based on monitored user behavior patterns, thereby adjusting parameters dynamically without increasing hardware complexity.
2Measurement precision
If multiple calibration profiles are maintained for different user characteristics, then gesture recognition accuracy improves, but the complexity of managing and switching profiles increases
Solution Approach 1:
The system performs self-calibration by automatically monitoring user gestures and selecting appropriate calibration profiles without requiring user intervention. The gesture processing circuitry autonomously detects when a switch in user characteristics occurs and switches profiles accordingly, eliminating the need for users to manually configure or understand calibration settings.
Solution Approach 2:
The system uses feedback from continuous gesture monitoring to automatically determine when to switch calibration profiles. By analyzing detected gesture patterns and comparing them against expected patterns for different profiles, the system provides automatic feedback-driven profile selection, improving ease of operation while maintaining multiple profiles for accuracy.
3Reliability
If the system automatically detects and responds to gesture pattern changes, then user experience improves, but processing time and computational resources increase
Solution Approach 1:
Multiple calibration profiles are pre-computed and stored before use. When the system needs to switch profiles, it simply selects from pre-prepared options rather than performing complex real-time calculations. This preliminary preparation reduces the time and computational resources required during actual gesture detection and profile switching.
Solution Approach 2:
The system uses predefined switching criteria and thresholds that allow for rapid profile transitions. By establishing clear decision boundaries in advance, the system can quickly determine when to switch profiles without extensive analysis, thereby minimizing the time lost during recalibration while maintaining reliable detection.
Data Source
AI summary
Embodiments are directed to gesture recognition in a computing device. Touch-based input by the user is monitored based on an output from a touch sensor. Gestures are directed from among the touch-based input. The detected gestures are analyzed to assign gesture characteristic profiles to the detected gestures according to profiling criteria. A sequential event log is tabulated representing counts of series of gestures based on assigned characteristic profiles and on temporal sequencing of the gestures. Circumstances for invocation of gesture detection re-calibration are assessed based on the tabulated series of gestures.