Hand Tracking Iterative Position Refinement
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Solution Overview
Problem
Current systems for tracking human hands in augmented reality environments face challenges in accurately determining hand feature positions, leading to inefficiencies in gesture-based input and interaction within these environments.
Innovation Solution
The system employs a method to iteratively determine hand feature positions using distancing devices, light sources, and optical elements, associating clusters of surfaces with hand features based on proximity, shape, and anatomical relationships, and adjusts sampling rates to refine position information, ensuring accurate tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative procedures are used to determine hand feature positions, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by predicting hand feature positions based on previous frame data and hand motion models before actual measurement is complete. This allows the iterative refinement process to start from a closer initial estimate, reducing the number of iterations needed and thus decreasing processing time while maintaining measurement precision.
Solution Approach 2:
The system implements periodic action by using fixed sampling rates for distance measurements and structured iteration cycles for position refinement. This periodic structure allows for predictable processing intervals and enables the system to balance between measurement precision and time loss by adjusting the number of iterations within each periodic cycle.
2Measurement precision
If sampling rate of distancing device is increased, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system applies dynamics by making the sampling rate adjustable rather than fixed. The sampling rate can be dynamically changed based on hand motion detection - higher sampling rates are used when hand motion is detected to maintain measurement precision, while lower sampling rates are used during stationary periods to reduce energy consumption. This dynamic adaptation resolves the contradiction between measurement precision and energy use.
3Reliability
If multiple iterations are performed for position determination, then reliability is improved, but productivity decreases
Solution Approach 1:
The system uses preliminary hand motion models and predictive algorithms to establish initial position estimates before iterative refinement begins. This preliminary action provides a close starting point that reduces the number of iterations required to achieve reliable results, thereby maintaining tracking accuracy while improving gesture-based input speed and overall productivity.
Solution Approach 2:
The system implements partial action by performing a minimum number of iterations required to achieve acceptable reliability rather than always performing maximum iterations. This allows the system to maintain adequate hand tracking accuracy while significantly reducing processing time and improving gesture-based input productivity, applying excessive action only when higher precision is specifically required.
Data Source
AI summary
A system configured for tracking a human hand in an augmented reality environment may comprise a distancing device, one or more physical processors, and/or other components. The distancing device may be configured to generate output signals conveying position information. The position information may include positions of surfaces of real-world objects, including surfaces of a human hand. Feature positions of one or more hand features of the hand may be determined through iterative operations of determining estimated feature positions of individual hand features from estimated feature position of other ones of the hand features.