Motion Model Update via Sensor Data Segmentation
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Solution Overview
Problem
Current motion analysis systems lack the ability to consistently and accurately distinguish different types or portions of motion, and they often lack tools to help analysts fully understand and improve motion data.
Innovation Solution
The system updates motion models using sensor data by receiving and processing data from motion sensors, generating training data based on user annotations, and performing experiments to validate or update the models, allowing for user input and feedback to refine the analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If crude comparisons using video data or motion data are used, then the system is simple to operate, but the measurement precision and reliability of motion analysis deteriorates
Solution Approach 1:
The patent segments motion data into distinct types or portions through automated classification algorithms. The system divides continuous sensor data into discrete motion events, categories, and patterns, enabling precise analysis without requiring complex manual intervention. This segmentation allows the system to achieve high measurement precision by focusing on specific motion characteristics rather than performing crude overall comparisons.
Solution Approach 2:
The patent introduces automated classification algorithms and machine learning models as intermediaries between raw sensor data and analysis results. These intermediaries process and interpret motion data automatically, eliminating the need for complex manual video analysis while maintaining high precision. The intermediary systems translate raw sensor readings into meaningful motion classifications and insights.
2Ease of operation
If automated analysis is performed with little to no user input, then the ease of operation improves, but the ability to consistently and accurately distinguish different types or portions of motion deteriorates
Solution Approach 1:
The patent implements self-service through automated classification algorithms that autonomously process motion data without requiring user intervention. The system automatically distinguishes different types and portions of motion by applying trained machine learning models to sensor data, achieving both ease of operation and reliable consistent results. The automated system serves itself by continuously learning from and adapting to motion patterns.
Solution Approach 2:
The patent utilizes parameter changes in machine learning models to improve motion distinction reliability. The system adjusts classification parameters, thresholds, and model weights based on training data and performance feedback, enabling automated analysis to achieve high consistency in distinguishing different motion types. These dynamic parameter adjustments allow the system to maintain reliability without user input.
3Loss of information
If current motion analysis techniques are used, then the device complexity remains low, but the loss of information about motion patterns and their interpretation increases
Solution Approach 1:
The patent implements feedback mechanisms where the system provides actionable insights and interpretations of motion patterns back to users. The automated classification algorithms generate detailed feedback about detected motions, including type, duration, intensity, and comparative analysis against established patterns. This feedback loop preserves comprehensive motion information while using sophisticated analysis tools to enhance rather than complicate the user experience.
Solution Approach 2:
The patent adds another dimension to motion analysis by transforming raw sensor data into enriched information through automated classification. The system elevates basic motion detection to comprehensive pattern recognition by incorporating temporal, spatial, and contextual dimensions. This dimensional transformation preserves detailed motion information while using complex analysis to extract meaningful insights without proportionally increasing user-facing complexity.
Data Source
AI summary
This disclosure relates to systems, media, and methods for updating motion models using sensor data. In an embodiment, the system may perform operations including receiving sensor data from at least one motion sensor; generating training data based on at least one annotation associated with the sensor data and at least one data manipulation; receiving at least one experiment parameter; performing a first experiment using the training data and the at least one experiment parameter to generate experiment results; and performing at least one of: update or validate a first motion model based on the experiment results.