3D Motion Capture Detection Zones for Gesture Normalization
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Solution Overview
Problem
Current motion-capture systems face inefficiencies due to excessive processing time and low detection sensitivity, as they often analyze the entire working volume regardless of the user's gesture region, wasting computational resources and failing to accurately infer gestural intent.
Innovation Solution
Implementing detection zones that are dynamically adjusted based on user behavior and gesture patterns, allowing for scaled and normalized parameters of user movements to be output, thereby reducing computational load and improving intent inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the motion-capture system analyzes the entire working volume to detect user gestures, then detection coverage is improved, but computational resources are wasted
Solution Approach 1:
The patent divides the working volume into multiple detection zones based on user position and gesture patterns. Instead of analyzing the entire working volume, the system segments it into relevant regions where gestures are likely to occur, thereby reducing computational load while maintaining detection coverage.
Solution Approach 2:
The system applies different processing quality levels to different regions of the working volume. Detection zones are established with higher processing priority in areas where user gestures are most likely to occur, while other regions receive reduced processing, optimizing the balance between detection reliability and computational efficiency.
2Reliability
If the detection zone size is increased to capture all possible gestures, then gesture detection capability is improved, but processing time increases
Solution Approach 1:
The detection zone dimensions are dynamically adjusted based on user position, gesture type, and historical gesture patterns. The system expands detection zones when gestures are likely to occur and contracts them when not needed, thereby reducing processing time while maintaining detection capability.
Solution Approach 2:
The system establishes detection zones in advance based on predicted user gesture regions before actual gestures occur. By pre-defining these zones based on user position and typical gesture patterns, the system avoids the need to analyze the entire working volume during gesture execution, reducing processing time.
3Measurement precision
If normalized parameters are provided for all users' gestures, then gesture interpretation accuracy is improved, but system complexity increases
Solution Approach 1:
The system transforms raw gesture data into normalized parameters that are scaled to a common reference frame. By changing the parameter representation from absolute coordinates to normalized values based on detection zone dimensions, the system improves gesture interpretation accuracy across different users and distances while managing complexity through standardized transformations.
Data Source
AI summary
Systems and methods are disclosed for detecting user gestures using detection zones to save computational time and cost and/or to provide normalized position-based parameters, such as position coordinates or movement vectors. The detection zones may be established explicitly by a user or a computer application, or may instead be determined from the user's pattern of gestural activity. The detection zones may have three-dimensional (3D) boundaries or may be two-dimensional (2D) frames. The size and location of the detection zone may be adjusted based on the distance and direction between the user and the motion-capture system.