Motion Accuracy Evaluation Using Dynamic Time Warping Alignment
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Solution Overview
Problem
Current systems for analyzing human motion are limited in quantitatively evaluating the accuracy of movements, often requiring extensive training data and struggling to correct deviations in complex sequences, especially when reference data is scarce or difficult to obtain.
Innovation Solution
A method and system that aligns test movements with reference movements using dynamic time warping, generating visualizations to encode quantitative accuracy, allowing for the identification of deviations and providing guidance for correction, even with minimal training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion capture technology and computer vision are used to generate three-dimensional models to identify pose or position and detect changes to identify motion or gesture, then human movements can be identified, but quantitative computation of accuracy is limited
Solution Approach 1:
The system segments the continuous motion capture data into discrete movements by detecting deviations from reference movements. Each deviation represents a distinct movement event that can be individually analyzed and quantified, transforming complex continuous data into manageable discrete units for accurate measurement.
Solution Approach 2:
Instead of trying to directly measure and quantify complex human movements from scratch, the system inverts the approach by first establishing reference movements and then detecting deviations from those references. This inversion simplifies the measurement process by comparing test movements against known standards rather than attempting to define accuracy from raw motion data alone.
2Measurement precision
If extensive training data is collected to establish reference movements, then movement accuracy can be evaluated, but data collection time and system complexity increase
Solution Approach 1:
The system performs preliminary action by establishing reference movements before actual measurement and evaluation takes place. These reference movements serve as pre-defined standards that enable immediate comparison and quantification of test movements without requiring extensive real-time data collection during the measurement process.
Solution Approach 2:
The system creates simplified copies of reference movements that can be stored and reused for comparison. Instead of collecting extensive training data each time, the system uses representative reference movement patterns that capture the essential characteristics needed for accurate measurement, reducing data collection requirements while maintaining measurement precision.
3Reliability
If reference data is obtained from experienced workers, then movement standards can be established, but the system struggles when reference data is scarce or difficult to obtain
Solution Approach 1:
The system performs self-service by automatically detecting and identifying movements through deviation analysis without requiring manual annotation or extensive human input. The algorithm autonomously processes motion capture data, compares it against reference movements, and identifies deviations, enabling the system to function effectively even when reference data is scarce or difficult to obtain.
Solution Approach 2:
The system adapts to different data availability conditions by changing its operational parameters. When reference data is abundant, it uses comprehensive comparison methods; when reference data is scarce, it adjusts to work with limited references while maintaining reliable measurement through its deviation-detection algorithm.
4Measurement precision
If manual observation of hand positions is used to evaluate movements, then outcome accuracy can be assessed, but the system cannot identify when or how deviations occurred during the movement
Solution Approach 1:
The system maintains continuous monitoring of movement throughout its execution rather than taking discrete snapshots. By continuously comparing actual movement trajectories against reference movements, the system captures temporal information about when and how deviations occur, preserving the continuous nature of motion data while enabling precise identification of deviation points and characteristics.
Data Source
AI summary
A system for determining a quantitative accuracy of a test movement relative to a reference movement includes a display output device, a memory, and a processor operatively connected to the display output device and the memory. The memory stores motion capture data and programming instructions. The processor executes the programming instructions to determine a quantitative accuracy of the test movement relative to the reference movement. A method, executable by the processor, for determining the quantitative accuracy includes receiving, with the processor, motion capture data that includes the reference movement and the test movement. The motion data is split into individual movements, and the test movement is aligned with the reference movement. The processor computes a quantitative accuracy of the test movement relative to the reference movement, and generates, with the display output device, a visualization representative of the test movement. The computed accuracy is encoded into the visualization.