Deformable Controller Gesture Correlation via ML Pattern Distortion
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Solution Overview
Problem
Current computer simulations, such as games, lack intuitive and engaging input methods that effectively utilize user gestures and deformable controllers to enhance interaction and immersion.
Innovation Solution
A system that includes a hollow hand-grippable object with a deformable interior pattern, where the distortions in the pattern are correlated to specific commands using machine learning models, allowing users to input commands to computer simulations through the deformation of the object, such as squeezing or twisting, which are then translated into directional or game-related commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional input devices are used for computer simulations, then the device complexity is low, but the ease of operation and user engagement are reduced
Solution Approach 1:
The controller uses a deformable elastic body that can dynamically change shape in response to user manipulation. The elastic body deforms when squeezed, stretched, or twisted, and returns to its original shape when released, providing a dynamic input mechanism that enhances user engagement while maintaining relatively simple device structure
Solution Approach 2:
The patent replaces traditional mechanical input mechanisms (buttons, joysticks) with an optical sensing system. An array of sensors detects the deformation of the elastic body and converts it into input signals, substituting complex mechanical linkages with a more elegant sensor-based approach that improves ease of operation
2Measurement precision
If deformable controllers with interior patterns are used, then the measurement precision of gestures is improved, but the device complexity increases
Solution Approach 1:
The patent uses an array of sensors that create a digital copy or map of the elastic body's interior surface. By detecting which sensors are activated and their activation patterns, the system can precisely determine the type and location of deformation without requiring complex mechanical structures
Solution Approach 2:
The interior surface of the elastic body is divided into multiple discrete sensor locations arranged in an array. This segmentation allows the system to detect deformations at multiple points simultaneously, improving measurement precision by identifying both the type and position of gestures through the pattern of activated sensors
Data Source
AI summary
Images of distortions in a pattern on the interior of a hollow hand-grippable object are input to a machine learning (ML) model, which is trained to return a command responsive to the inputting. The command is used for controlling play of at least one computer simulation.