Touch Sensor Calibration via Periodic Scanning
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Solution Overview
Problem
Current wireless control devices lack efficient calibration methods and effective means to differentiate between intentional and unintentional user inputs, leading to potential inaccuracies and increased power consumption.
Innovation Solution
A method and system for calibrating touch sensors in input devices that include a calibration process initiated after a predetermined period of no user activity, using a combination of sensors like optical, touch, accelerometer, and gyroscope to detect user inputs and adjust gesture recognition thresholds based on conditions such as lift detection, speed, and button press, thereby improving accuracy and reducing power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous scanning of touch sensor is performed to detect user inputs, then detection accuracy is improved, but power consumption increases
Solution Approach 1:
The system implements periodic scanning of the touch sensor at different intervals based on operational state. During calibration mode, scanning occurs at longer intervals (e.g., every few seconds) rather than continuously, reducing power consumption while maintaining detection capability. The scanning frequency is adjusted dynamically based on whether the device is in normal operation or calibration mode.
Solution Approach 2:
The system performs calibration during periods of predicted low activity to prepare accurate detection parameters in advance. By detecting lack of user activity for a threshold period and then initiating calibration, the system ensures accurate touch detection is established before normal use begins, avoiding the need for continuous high-frequency scanning throughout operation.
2Measurement precision
If calibration is performed continuously to maintain accuracy, then measurement precision is improved, but loss of time increases
Solution Approach 1:
Calibration is performed periodically based on detected user activity patterns rather than continuously. The system monitors for periods of predicted low activity (when no user inputs are detected for a threshold period) and initiates calibration during these windows, ensuring accuracy is maintained without constant time loss to calibration procedures.
Solution Approach 2:
The system performs calibration in advance during predicted low-activity periods, preparing the detection parameters before they are needed for normal operation. This preliminary calibration ensures accurate gesture recognition is ready when users resume active use, avoiding delays during actual interaction.
3Reliability
If multiple sensors are used to detect user inputs, then reliability is improved, but device complexity increases
Solution Approach 1:
The system divides detection into separate functional modules: optical sensor for motion tracking, touch sensor for contact detection, accelerometer for orientation/movement, and gyroscope for rotational data. Each sensor type handles specific detection tasks independently, and their outputs are integrated by the processor. This segmentation improves reliability through multiple independent detection channels while managing complexity through modular architecture.
Solution Approach 2:
The processor serves multiple functions: it processes data from all sensor types, performs calibration of the touch sensor, detects user inputs, determines operational mode (normal vs. calibration), and controls power management. This multi-functionality consolidates complexity into a single processing unit rather than requiring separate dedicated processors for each function.
4Adaptability or versatility
If threshold values are adjusted dynamically based on conditions, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system dynamically adjusts gesture recognition thresholds based on detected operational conditions such as device orientation, motion state, and calibration mode. Instead of fixed thresholds, the processor modifies threshold values in real-time based on sensor inputs and current operational state, enabling the system to adapt to different usage scenarios while maintaining manageable complexity through rule-based adjustment logic.
Data Source
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AI summary
A method for applying a first or second set of threshold values for touch sensor gesture recognition based on the presence of one of a plurality of predetermined conditions is described herein. The plurality of predetermined conditions can include one or more of a lift detection, a speed threshold detection, and a button press detection. Lift detection can be detected by one or more of an optical sensor, gyroscope, or accelerometer. Speed detection can be detected by one or more of an optical sensor, gyroscope, or accelerometer. Button detection can be detected by a mechanical button, pressure sensitive button, or a touch sensor.