Foot Last Modeling System Using Statistical Data Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current shoe manufacturing processes rely on generic foot last designs based on target shoe sizes, neglecting geographical and individual foot shape variations, leading to inadequate fit and comfort.
Innovation Solution
A data-driven system utilizing a graphical user interface to select and generate 3D models of feet or foot lasts based on statistical measurements, including geographic region, gender, and percentile scores, employing machine learning and nearest neighbor algorithms to identify and modify anatomical points for a precise fit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If generic foot last designs based on target shoe sizes are used, then manufacturing simplicity is maintained, but fit precision and comfort are insufficient
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing statistical foot measurement data from multiple geographic regions, genders, and percentiles. When a user requests a foot last, the system has already processed and organized this data into usable models, eliminating the need for real-time complex calculations and enabling quick generation of precise foot lasts.
Solution Approach 2:
The system changes parameters by allowing users to select specific geographic regions, genders, shoe sizes, and percentile values. These parameter selections dynamically adjust the statistical data used to generate the foot last, transforming a single generic design into multiple customized designs that precisely fit different population groups.
2Manufacturing precision
If customized foot models based on statistical measurements are generated, then fit precision is improved, but data processing complexity increases
Solution Approach 1:
The system creates simplified copies of real foot measurements by generating statistical representations from actual foot scan data. Instead of processing every individual measurement, the system creates representative foot last models that capture the essential characteristics of specific population groups, reducing data processing complexity while maintaining accuracy.
Solution Approach 2:
The system manages data processing complexity by organizing measurements into standardized parameters such as geographic region, gender, shoe size, and percentile. This parameterization allows the system to handle diverse data through a consistent framework, making complex statistical data manageable and easily retrievable.
3Adaptability or versatility
If geographical variations in foot shape are accounted for, then adaptability is improved, but model complexity increases
Solution Approach 1:
The system segments the global population into distinct geographic regions (e.g., North America, Europe, Asia, South America, Africa) with specific foot shape characteristics for each region. This segmentation allows the system to provide geographical adaptability by selecting the appropriate regional model, while keeping the overall system manageable through organized categorization.
Solution Approach 2:
The system incorporates geographical adaptability through parameter changes, where the selected geographic region directly influences the statistical data used to generate the foot last. By making geography a selectable parameter, the system efficiently adapts to different populations without requiring separate complex modeling systems for each region.
4Adaptability or versatility
If individual foot variations are modeled, then product versatility is improved, but manufacturing complexity increases
Solution Approach 1:
The system applies partial action by focusing on the most significant sources of foot variation (geographic region, gender, size, and percentile) rather than attempting to model every possible individual difference. This approach captures the essential variations needed for most applications while keeping the system manufacturable and manageable.
Solution Approach 2:
The system manages manufacturing complexity by using parameter changes to represent individual foot variations. Instead of creating unique models for every possible combination of foot characteristics, the system uses a set of standardized parameters (region, gender, size, percentile) that can be combined to represent diverse foot types, simplifying the manufacturing process.
Data Source
AI summary
Methods, systems, and non-transitory computer readable media for providing a user portal to facilitate modeling of foot-related data are described.