Inadvertent Gesture Control Detection via Sensor Thresholds
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Solution Overview
Problem
Current gesture control systems inadvertently activate due to unintended user movements, leading to usability issues, as they capture all user activity and translate it into machine or application control without distinguishing between intended and unintended actions.
Innovation Solution
Implementing a method that captures image data using an image capture device and analyzes it with a processor to identify and determine if a gesture control is inadvertent, then disregarding such controls to prevent unintended activation, by using thresholds for feature location, time ranges, and contextual information from other input modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the gesture control system captures all user activity and translates it into machine control, then the system responsiveness and control capability are improved, but the system becomes prone to inadvertent activations and usability issues
Solution Approach 1:
The system uses feedback from multiple sensors (accelerometer, gyroscope, proximity sensor) to continuously monitor and analyze user movements. By processing this feedback information and comparing it against predefined gesture patterns, the system can distinguish between intentional gestures and inadvertent movements, thereby maintaining high responsiveness while reducing false activations.
Solution Approach 2:
The system changes parameters such as threshold values for gesture detection, time windows for gesture validation, and sensitivity levels based on contextual information from multiple sensors. By dynamically adjusting these parameters, the system optimizes its detection accuracy to differentiate between deliberate user actions and accidental movements.
2Measurement precision
If the system uses multiple sensors and contextual analysis to distinguish intended from unintended gestures, then the accuracy of gesture recognition is improved, but the device complexity increases
Solution Approach 1:
The patent reuses existing multi-functional sensors (accelerometer, gyroscope, proximity sensor) that serve both gesture control and other device functions. By leveraging these universal components for gesture recognition, the system achieves high measurement precision without significantly increasing device complexity, as the sensors are already integrated into the device for other purposes.
Solution Approach 2:
The system uses the device's own existing sensors and processing capabilities to perform gesture recognition, rather than requiring external specialized equipment. The processor leverages contextual information from sensors already present in the device, enabling accurate gesture differentiation while avoiding additional hardware complexity.
3Reliability
If the system processes and analyzes image data and sensor information in real-time, then the ability to detect inadvertent gestures is improved, but the energy consumption increases
Solution Approach 1:
The system performs partial processing by analyzing only the necessary sensor data and image information required for gesture recognition, rather than processing all available data. By selectively processing relevant information and using predefined gesture patterns for comparison, the system achieves reliable inadvertent gesture detection while minimizing energy consumption through optimized processing scope.
Data Source
AI summary
An embodiment provides a method, including: capturing, using an image capture device, image data; analyzing, using a processor, the image data to identify a gesture control; determining, using a processor, the gesture control was inadvertent; and disregarding, using a processor, the gesture control. Other aspects are described and claimed.