Virtual Desktop Transformation for Personalized Cursor Control
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Solution Overview
Problem
The variability in human movement interpretation by gaming systems leads to error-prone interactions, resulting in an unsatisfactory gaming experience due to the lack of personalized interface control calibration.
Innovation Solution
The implementation of a virtual desktop transformation that dynamically adjusts based on a history of button press actions, allowing for personalized calibration of the virtual interface space to match the user's natural movement style, thereby improving the accuracy of cursor movements and reducing 'slippage' during button presses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed virtual desktop transformation is used to translate hand position to cursor position, then the system is simple to operate, but the interpretation of user movements becomes error-prone due to variability in natural human movement styles
Solution Approach 1:
The virtual desktop transformation is changed from a fixed static mapping to a dynamic adaptive mapping that adjusts based on observed user behavior. The system continuously learns the user's natural movement characteristics through repeated interactions and modifies the transformation parameters accordingly, making the cursor movement more consistent with the user's intuitive pushing motions.
Solution Approach 2:
The system automatically calibrates and adjusts the virtual desktop transformation without requiring explicit user intervention or manual configuration. Through passive observation of user interactions, the system self-adjusts the transformation parameters to match the user's natural movement style, eliminating the need for separate calibration procedures.
2Measurement precision
If the virtual desktop transformation is dynamically adjusted based on user history, then the personalized calibration improves accuracy, but the system complexity increases due to additional processing requirements
Solution Approach 1:
The system implements a feedback loop where user interactions (hand movements and corresponding cursor actions) are continuously monitored and fed back into the transformation model. This feedback mechanism allows the system to learn from actual usage patterns and refine the virtual desktop transformation parameters over time, improving precision through iterative optimization.
Solution Approach 2:
The system performs preliminary calibration by observing and storing a history of user interactions before making adjustments to the transformation. By accumulating sufficient data about user movement patterns in advance, the system can establish an accurate personalized model that improves precision without requiring complex real-time calculations during actual use.
3Adaptability or versatility
If depth images are processed to track hand position in world space, then the system can capture natural user movements, but the processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential information needed for transformation calibration from the depth images, specifically focusing on hand position and movement trajectory. By selectively extracting relevant features rather than processing the entire image data, the system reduces computational overhead while maintaining the ability to capture natural user movements.
Data Source
AI summary
A computing system is configured to receive one or more depth images, from the depth camera, of a world space scene including a human target. The computing system translates a world space position of a hand of the human target to a screen space cursor position of the user interface using a virtual desktop transformation. The computing system also dynamically adjusts the virtual desktop transformation based on a history of button press actions executed by the human target.