Multi-Modal Hand Tracking for Game Controller Input
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Solution Overview
Problem
Existing input detection systems for hand-held controllers, such as those used in video games, rely on single modes of data which lead to inaccurate and error-prone interpretations of finger gestures.
Innovation Solution
A multi-modal finger tracking system using a custom ensemble model trained with data from multiple sensors and components, including IMU, wireless signals, sound, and image capturing, to accurately verify finger gestures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single mode of data (e.g., image tracking) is used to detect finger gestures, then the device complexity is reduced, but the measurement precision and reliability of gesture detection deteriorate
Solution Approach 1:
The patent combines multiple data modalities (image data from cameras, inertial data from IMU sensors, wireless signal data) into a unified gesture detection system. The ensemble model integrates these diverse data sources to detect and verify finger gestures, achieving higher accuracy than any single modal system could provide alone.
Solution Approach 2:
The system uses a multi-modal data collection framework that can process various types of data from different sensors (visual, inertial, wireless) through a single unified model. This universal approach allows the same gesture detection system to leverage multiple data sources without requiring separate specialized systems for each modal type.
2Reliability
If multiple sensors and components are used to collect multi-modal data, then the measurement precision and reliability of finger gesture detection improve, but the device complexity increases
Solution Approach 1:
Multiple sensor types (cameras, IMU sensors, wireless communication devices) are merged into a coordinated system where data from all sources is collected simultaneously and processed by the ensemble model. This integration allows the system to achieve high reliability through redundant verification while managing complexity through unified processing.
Solution Approach 2:
The system employs feedback mechanisms where the ensemble model continuously refines its predictions by comparing results from different data modalities. The model uses feedback from multiple sensor readings to verify and correct gesture interpretations, improving reliability through iterative verification rather than relying on any single sensor alone.
3Measurement precision
If a custom ensemble model is trained using multiple modalities of data, then the measurement precision of finger gesture interpretation improves, but the manufacturing precision and training complexity increase
Solution Approach 1:
The system performs preliminary training of the ensemble model using a comprehensive dataset of multi-modal gesture data collected from multiple users and scenarios. This preliminary training phase establishes the foundational accuracy of the model before deployment, ensuring that the model learns correct gesture interpretations from diverse data sources during the manufacturing phase.
Solution Approach 2:
The training process involves adjusting multiple parameters simultaneously to optimize the ensemble model's performance across different data modalities. The system changes model parameters, weighting factors, and training criteria to achieve optimal balance between accuracy and training complexity, allowing high measurement precision without proportionally increasing manufacturing precision requirements.
Data Source
AI summary
Methods and systems are provided for verifying an input provided at a controller including detecting a finger gesture on a surface of the controller. Responsive to detecting the finger gesture, multi-modal data is collected from a plurality of sensors and components tracking the finger gesture. The multi-modal data is used to generate an ensemble model using machine learning algorithm. The ensemble model is trained in accordance to training rules defined for different finger gestures. An output is identified from the ensemble model for the finger gesture. The output is interpreted to define an input for an interactive application selected for interaction.