Hybrid Controller Engagement Detection for VR Input Switching
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Solution Overview
Problem
Existing headsets for virtual, augmented, and mixed reality lack the ability to seamlessly switch between controller and hand interaction modes, leading to inefficiencies and increased power consumption due to reliance on manual user input.
Innovation Solution
A hybrid sensor approach using image recognition, motion data, and machine learning models to detect and track controllers and hands, determining the spatial relationship between them, and automatically switch input modes based on proximity and geometric checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual user input is used to switch between controller and hand interaction modes, then the system can respond to user intentions, but it increases power consumption and reduces efficiency
Solution Approach 1:
The system automatically detects whether the user is holding a controller or using hand gestures by analyzing spatial relationships between hands and controller using cameras and machine learning models. This self-service detection eliminates the need for manual mode switching input from the user, thereby reducing power consumption while maintaining efficient interaction mode transitions
Solution Approach 2:
The system continuously monitors the spatial relationship between the user's hands and the controller using camera feedback and machine learning classification. This real-time feedback mechanism automatically switches interaction modes based on detected hand-controller proximity and orientation, improving switching efficiency without requiring additional user input
2Measurement precision
If the system tracks both controller and hand positions continuously, then accurate engagement detection is achieved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary tracking of hand and controller positions throughout the frame, then uses machine learning models to classify engagement state based on pre-computed spatial relationships. This preliminary action approach ensures accurate engagement detection while optimizing processing by preparing data structures that facilitate rapid classification without requiring continuous heavy computation during the detection moment
Solution Approach 2:
The system changes the parameter of spatial relationship analysis by focusing on key geometric features such as hand-to-controller distance, relative orientation, and hand pose configuration. By transforming the tracking data into these specific geometric parameters, the system achieves accurate engagement detection with reduced computational complexity compared to analyzing all pixel-level hand and controller data
Data Source
AI summary
Techniques are disclosed that enable an electronic device (e.g., a headset) to track a controller and one or more hands of a user wearing the headset by using image recognition and various sensors (e.g., image sensors, motion sensors and proximity sensors) to determine whether the user has picked up and is holding the controller. The input mode of the headset can be switched accordingly.