3D Motion Analysis Using Personalized Reference Data
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Solution Overview
Problem
Conventional systems for 3D motion analysis in sports and health applications require manual comparison of captured data to reference data, necessitating a deep understanding of optimal human movement and being time-consuming, as they lack automated personalized feedback.
Innovation Solution
An intelligent analysis framework using machine learning algorithms to create a personalized reference data subset based on user characteristics, providing dynamic recommendations for improving motion without manual data filtering, utilizing 3D motion capture data from sensors like inertial and optical systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparison of captured data to reference data is used, then analysis accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs automated data analysis and comparison without requiring manual operator intervention. The machine learning model automatically processes captured motion data, compares it to reference data, and generates feedback, enabling the system to serve itself and eliminating time-consuming manual operations while maintaining analysis accuracy
Solution Approach 2:
The patent replaces the manual mechanical process of data comparison with an automated computational system using machine learning algorithms. The machine learning model substitutes human operators in performing data analysis, automatically identifying movement patterns and providing feedback, thereby reducing time consumption while preserving or enhancing analysis precision
2Productivity
If personalized reference data subset is created using machine learning, then analysis efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the reference data into personalized subsets based on user characteristics. The machine learning model divides the large reference dataset into smaller, relevant portions tailored to each user's specific needs, movements, and goals. This segmentation improves analysis efficiency by focusing computational resources on relevant data while managing system complexity through organized data structures
Solution Approach 2:
The system dynamically adjusts parameters of the reference data subset based on user characteristics detected by the machine learning model. By changing parameters such as data selection criteria, comparison thresholds, and feedback parameters based on individual user profiles, the system achieves personalized efficient analysis without requiring complex manual configuration
Data Source
AI summary
A processing device receives three dimensional (3D) motion capture data corresponding to a subject user performing a physical activity and receives attribute data associated with the subject user. The processing device determines a personalized reference data set for the subject user based on 3D motion capture data associated with a group of users performing the physical activity, wherein each user from the group of users shares at least a portion of the first attribute data with the subject user. The processing device provides the personalized reference data set as an input to a trained machine learning model and obtains an output of the trained machine learning model, wherein the output comprises a recommendation for the subject user pertaining to improvement of the physical activity.