3D Surface Shape Evaluation for Customized Saddle Fitting
Solution Overview
Problem
Existing methods for evaluating the three-dimensional shape of surfaces, such as a horse's saddle area, are limited as they only use 3D data from a single point in time, lacking diversity in evaluation and failing to account for changes or comparisons over time or between individuals.
Innovation Solution
A method that evaluates 3D data sets from multiple points in time or between different individuals, incorporating additional information for personalized recommendations on saddle or shoe fitting, and uses these data sets to manufacture customized auxiliary devices like compensation pads, enhancing evaluation possibilities and material efficiency in fitting devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only 3D data from a single point in time is used for evaluation, then the evaluation process is simple, but the evaluation possibilities and diversity are limited
Solution Approach 1:
The system transitions from static single-time-point evaluation to dynamic multi-time-point evaluation. By storing and comparing 3D data sets taken at different times, the system enables tracking of shape changes over time, allowing evaluation of developmental trends, regression detection, and progressive adaptation of fitting devices.
Solution Approach 2:
The evaluation system is extended to serve multiple functions: initial fitting assessment, progress monitoring over time, regression detection, predictive prognosis, and training guidance. This multi-functionality is achieved by incorporating temporal dimensions and comparing multiple data sets within a unified evaluation framework.
2Measurement precision
If 3D data from multiple data sets are used for evaluation, then evaluation possibilities and detection accuracy improve, but data processing complexity increases
Solution Approach 1:
The system implements feedback mechanisms by comparing current 3D data sets with historical data sets. This enables detection of deviations from expected development patterns, provides feedback on training effectiveness, and allows for predictive prognosis by identifying trends. The feedback loop enhances detection accuracy while managing complexity through structured comparison protocols.
Solution Approach 2:
The system performs preliminary actions by establishing baseline 3D data sets and creating reference models in advance. These pre-established references enable more efficient processing of subsequent data sets, as comparisons can be made against predetermined standards rather than requiring complex multi-dimensional analysis of all historical data simultaneously.
3Reliability
If additional information is incorporated into 3D data sets, then personalized recommendations and evaluation quality improve, but data management complexity increases
Solution Approach 1:
The data structure is organized in a nested manner where core 3D shape data is surrounded by additional information layers. This includes nested metadata about measurement conditions, device information, operator details, and contextual data. This hierarchical organization allows efficient access to different levels of detail and simplifies data management by grouping related information together.
Solution Approach 2:
The system creates standardized data templates and reference models that can be copied and adapted for different evaluation scenarios. These templates include predefined structures for additional information, ensuring consistency in data collection while reducing the burden of creating custom data management schemas for each case.
Data Source
AI summary
A method is disclosed for evaluating data representing the three-dimensional shape of at least a portion of the surface of a human being or animal, in the following referred to as 3D data, wherein 3D data of at least two data sets, each representing the three-dimensional shape of at least a portion of the surface of a human being or animal and each taken from a database of three-dimensional shapes, in the following referred to as 3D data sets. Auxiliary devices may be manufactured based on 3D data stored in the data base, and a method for obtaining the 3D data to be stored in the data base is also disclosed.