Granular Binarization for Extended Reality Sensor Data
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Solution Overview
Problem
Existing extended reality systems inadequately provide feedback on potential future states and fail to efficiently identify repetition behaviors leading to user errors, due to the high processing demands of large quantities of sensor data.
Innovation Solution
A system comprising a learning platform that generates repeat event records from historical sensor data, clusters them, and trains a prediction model to identify subsequent events, deploying a binarization filter and prediction model to a real-time platform to provide actionable feedback before events occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large quantities of sensor data are processed to identify repetition behaviors and provide feedback on future states, then user safety and interaction quality are improved, but processing time and computational resources are excessively consumed
Solution Approach 1:
The patent segments the continuous sensor data stream into discrete event records with specific features (event type, timestamp, duration, intensity). This segmentation allows the system to process only relevant event characteristics rather than raw continuous data, reducing computational load while maintaining safety monitoring capability. The segmentation into repeat event records further divides data by frequency patterns, enabling targeted analysis of potentially harmful repetition behaviors.
Solution Approach 2:
The system performs preliminary processing by pre-defining event types and their associated features before actual safety analysis. By establishing event templates with predetermined characteristics (type, timestamp, duration, intensity) in advance, the system avoids complex real-time analysis of raw sensor data. This preliminary structuring enables faster processing during operation while maintaining comprehensive safety monitoring.
2Measurement precision
If detailed analysis of sensor data is performed to identify repetition behaviors, then accuracy in predicting future events is improved, but device complexity and computational demands increase
Solution Approach 1:
The patent applies local quality by focusing analysis on specific event features (type, timestamp, duration, intensity) rather than processing all sensor data uniformly. Each event record is analyzed with targeted attention to its particular characteristics, allowing precise identification of repetition patterns without requiring complex global analysis of the entire data stream. This selective feature-based approach maintains prediction accuracy while reducing system complexity.
Solution Approach 2:
The system transforms raw sensor data into standardized event records with specific parameters (event type, timestamp, duration, intensity). By changing the data representation from continuous sensor readings to discrete parameterized events, the system enables more efficient pattern recognition. The parameterization allows direct comparison of event characteristics to identify repetitions without complex computational algorithms.
3Ease of operation
If real-time feedback is provided to users about potential future events, then user interaction quality is improved, but processing speed requirements and computational resources increase
Solution Approach 1:
The patent extracts only the essential features needed for safety analysis from the complete sensor data stream. By taking out and isolating specific event characteristics (type, timestamp, duration, intensity) that are relevant to repetition pattern identification, the system avoids processing unnecessary data. This extraction approach enables real-time feedback generation with reduced computational resource requirements while maintaining high interaction quality.
Data Source
AI summary
A system may receive events derived from historical sensor data. The system may generate repeat event records based on the events. The system may cluster the repeat event records into repeat event clusters. The system may determine filter ranges for the repeat event clusters. The system may generate a binarization filter comprising instructions to generate a binary sequence indicative of at least one of the filter ranges. The system may execute the binarization filter to generate binary sequences corresponding to the repeat event records, respectively. The system may train a prediction model based on the binary sequences. The system may deploy the binarization filter and the prediction model to a real-time platform.