Dynamic Baseline Selection for Side Touch Gesture Detection
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Solution Overview
Problem
Existing input devices face challenges in accurately identifying gestures along a single axis, particularly when input objects are proximate to the sides of the device, as they struggle to differentiate between static and dynamic capacitive sensing signals effectively.
Innovation Solution
The implementation of a processing system that utilizes sensor electrodes to generate capacitive sensing signals, selects a baseline from previous frames, calculates delta measurements, and identifies gestures by removing oldest frames from the buffer, allowing for improved detection of tapping and swiping gestures along a single axis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If capacitive sensing signals are used to detect gestures along a single axis, then gesture detection capability is enabled, but the ability to differentiate between static and dynamic signals deteriorates
Solution Approach 1:
The system performs preliminary action by storing multiple previous capacitive sensing frames in a frame buffer before current processing. This allows the selection of an appropriate baseline frame from historical data that best represents the static state, enabling effective differentiation between static and dynamic signals in the current frame.
Solution Approach 2:
The system applies dynamics by making the baseline selection adaptive rather than fixed. The baseline selector dynamically chooses which previous frame to use as baseline based on current signal characteristics, allowing the system to adapt to changing conditions and maintain precision in distinguishing static versus dynamic gestures.
2Measurement precision
If a frame buffer stores multiple previous capacitive frames for baseline selection, then gesture recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The system extracts only the essential functionality needed for baseline selection - storing previous frames and selecting the most appropriate one - while leaving the actual gesture recognition and complex processing to separate components. This modular extraction reduces overall system complexity while maintaining recognition accuracy.
Solution Approach 2:
The frame buffer serves multiple purposes: it stores historical data for baseline selection, provides reference frames for comparison, and enables adaptive processing. This self-service approach allows the same stored data to support multiple functions, reducing the need for additional components and simplifying the overall system architecture.
3Measurement precision
If baseline selection from previous frames is implemented, then static and dynamic gesture differentiation is enhanced, but processing time increases
Solution Approach 1:
The frame buffer is pre-filled with previous capacitive sensing frames during normal operation, so when baseline selection is needed, the data is already available. This preliminary storage eliminates the need for time-consuming data collection during the actual gesture processing, reducing processing time while maintaining accuracy.
Solution Approach 2:
The system uses a limited number of recent frames (typically 1-5 frames) in the frame buffer rather than storing extensive historical data. This approach provides sufficient baseline selection capability while minimizing memory usage and processing overhead, achieving a balance between accuracy and processing efficiency.
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 enhances the accuracy and usability of input devices by effectively distinguishing between static and dynamic gestures, enabling precise gesture recognition along a single axis, such as the side of the device.
Implementation Method 1
a plurality of sensor electrodes in a sensing region of the input device... receive a plurality of capacitive sensing signals generated using the plurality of sensor electrodes
Data Source
AI summary
Input devices described herein include sensing regions that permit the device to identify a location of input objects along a side of the device. To perform gesture detection, an input device selects baseline signals which are subtracted from capacitive sensing signals captured using the sensor electrodes in the sensing region. Doing so results in delta signals that represent the difference between the capacitive sensing signals and the baseline signals. In one embodiment, the baseline signals are selected from capacitive sensing signals received during previous capacitive frames. In one example, the input device uses the capacitive sensing signals received during the Nth previous capacitive frame as the baseline signals. Alternatively, the input device may determine which frame has capacitive sensing signals that vary the most from the current sensing signals and use that frame as the baseline.