Machine Learning-based Estimation of Foot Parameters for Custom Insole Production: A Comprehensive Analysis of Structural Measurements
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Abstract
Precise quantification of foot morphology is critical for clinical diagnostics, biomechanics, and personalized orthotic design. Conventional anthropometric methods often remain labor-intensive or dependent on specialized hardware, necessitating more efficient predictive frameworks. This study develops and validates a series of machine learning (ML) models to predict nine essential foot anthropometric parameters. Leveraging a dataset of 544 independent foot samples from 272 participants, encompassing high, normal, and flat arch types. The models were evaluated using a 10-fold cross-validation strategy to ensure robust generalizability. Our pipeline integrates correlation-based feature selection with hyperparameter-optimized regression algorithms, including XGBoost, Random Forest, Support Vector Regressors, Neural Networks, and Linear Regression. The results demonstrate high predictive fidelity, with Mean Absolute Errors (MAE) consistently remaining below 0.5 cm. This level of precision meets the 0.5 cm clinical tolerance threshold established through expert consultation for in-sole production, while simultaneously aligning with international footwear sizing increments, thereby confirming the framework's practical utility in real-world manufacturing. Although parameters such as the length from heel to midfoot area and the length from heel to distal metatarsal head achieved exceptional precision (MAE of 0.012 cm and 0.026 cm, respectively), predicting arch height remains a notable challenge. This research underscores the necessity of optimal feature engineering and algorithm selection in automating foot morphometric assessment.
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