Touch-Free Gesture Recognition Using Control Boundaries
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Solution Overview
Problem
Existing touch-free gesture detection systems are limited in the number of gestures they can recognize and act upon, hindering effective user interaction in various settings.
Innovation Solution
A touch-free gesture recognition system that uses a processor to receive image information from an image sensor, detect user gestures, and access information associated with control boundaries, enabling actions based on the detected gestures and their locations relative to these boundaries, employing machine learning techniques for enhanced detection and prediction of user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional touch-based input devices are used, then user interaction is simple and reliable, but the system lacks versatility in interaction methods
Solution Approach 1:
The patent replaces traditional mechanical touch-based input devices with an optical-based gesture recognition system. Image sensors capture visual information of user gestures, and processors analyze these images to detect and interpret gestures without physical contact. This substitution enables diverse interaction methods (various gesture types) while eliminating the need for multiple physical input devices, thus improving versatility without proportionally increasing system complexity.
Solution Approach 2:
The gesture recognition system serves multiple functions: it detects various gesture types (hand movements, finger gestures, arm movements), works across different applications and settings, and can replace multiple traditional input devices. This multi-functionality approach allows a single system to provide diverse interaction methods, improving adaptability while maintaining manageable complexity through unified processing architecture.
2Adaptability or versatility
If the number of detectable touch-free gestures is increased, then user interaction versatility improves, but detection accuracy and reliability decrease
Solution Approach 1:
The patent segments gesture detection into distinct categories and types (hand gestures, finger gestures, arm movements, wrist rotations). Each gesture type has specific detection criteria and parameters. This segmentation allows the system to handle multiple gesture types systematically, maintaining detection accuracy for each category while collectively providing versatile gesture recognition capability across different interaction scenarios.
Solution Approach 2:
The system applies different detection parameters, sensitivity levels, and analysis methods tailored to specific gesture types. For example, finger gestures may use different detection thresholds than arm movements. This localized optimization ensures high detection accuracy for each gesture category while enabling comprehensive multi-gesture recognition, thus resolving the contradiction between versatility and reliability.
3Measurement precision
If machine learning techniques are employed to predict user behavior, then gesture detection accuracy improves, but processing time and computational requirements increase
Solution Approach 1:
The system performs preliminary processing of image data before applying complex machine learning algorithms. Basic gesture features are extracted and pre-processed, and obvious gestures are identified using simpler methods first. This preliminary action reduces the computational burden on machine learning models, decreasing processing time while maintaining high detection accuracy for complex gestures that require advanced analysis.
Solution Approach 2:
The system applies machine learning techniques selectively rather than to all gestures uniformly. For common or simple gestures, traditional detection methods are used. Machine learning is applied primarily to ambiguous, complex, or novel gestures where enhanced accuracy is needed. This partial application of computationally intensive methods reduces overall processing time while improving accuracy where it matters most.
Data Source
AI summary
Systems, methods and non-transitory computer-readable media for triggering actions based on touch-free gesture detection are disclosed. The disclosed systems may include at least one processor. A processor may be configured to receive image information from an image sensor, detect in the image information a gesture performed by a user, detect a location of the gesture in the image information, access information associated with at least one control boundary, the control boundary relating to a physical dimension of a device in a field of view of the user, or a physical dimension of a body of the user as perceived by the image sensor, and cause an action associated with the detected gesture, the detected gesture location, and a relationship between the detected gesture location and the control boundary.