Motion Data Units for Accurate Real-Time Analysis
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Solution Overview
Problem
Existing motion sensing and processing systems rely on statistical models for analysis, which lose data integrity and accuracy in quantifying real-time motion, especially when comparing real-time data to known motions.
Innovation Solution
Processing motion data into units that retain strength information, allowing for efficient and accurate comparison with actual measured data in motion libraries, enabling qualitative and quantitative analysis of real-time motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If statistical models are used for motion data analysis, then processing efficiency is improved, but data integrity and measurement precision deteriorate
Solution Approach 1:
The patent creates a motion library containing copies of actual measured motion data from multiple sources. These data copies are stored and used for direct comparison with real-time motion data, replacing statistical models while maintaining processing efficiency through pre-organized data structures and similarity algorithms.
Solution Approach 2:
The system performs preliminary data collection and organization by gathering actual motion data from various sources, processing it into standardized formats, and storing it in a motion library before real-time analysis is needed. This pre-processing enables fast comparison during actual motion analysis without sacrificing data integrity.
2Speed
If statistical models are used for motion comparison, then analysis speed is improved, but accuracy in quantification deteriorates
Solution Approach 1:
Instead of using statistical abstractions, the system stores and compares against actual measured motion data copies in the motion library. This allows direct comparison that preserves quantification accuracy while achieving speed through efficient data retrieval and similarity matching algorithms.
Solution Approach 2:
The motion data in the library is segmented into discrete motion patterns and organized by motion type, making it easier to quickly retrieve relevant comparison data during real-time analysis, thus maintaining both speed and accuracy.
3Reliability
If actual measured data is stored in a motion library, then data integrity is maintained, but storage requirements and processing complexity increase
Solution Approach 1:
The motion library serves multiple functions: storing actual measured data for integrity, organizing it by motion type for efficient retrieval, and enabling both qualitative and quantitative analysis. This multi-functionality reduces overall system complexity despite the detailed data storage.
Solution Approach 2:
The system transforms raw motion data into standardized parameters and formats before storing in the motion library. This parameter transformation maintains data integrity while reducing processing complexity during real-time analysis by working with normalized data structures.
Data Source
AI summary
Systems for studying motion are provided. A computing device [10] has a processor [20], an accelerometer [11], a gyroscope [12], a magnetometer [13] and storage/computer readable media [30] in communication with one another. The computing device [10] can sense, classify, qualify and/or quantify real-time motion data of a moving target against classified initial motion data in a motion library [32]. Motion data is processed as particularized units of motion. The computing device [10] may use machine learning algorithms for “training” and “learning.” The computing device [10] can be used in many industries, including the fitness industry, where computing device [10] can be used with wearable technology.