Footprint to prediction: low-cost, three-class foot arch screening using a Harris mat

Arch type foot pathologies of Pes Cavus (high arch), Pes Planus (flatfoot), and Normal Arch patterns are good predictors of foot health that affect gait, posture, and predispose to musculoskeletal disorders. The research work described herein advances a computer-assisted automated classification of the foot arch from footprint images obtained on the Harris mat and transformed into contact-area visualizations through image processing, then classified using machine learning approaches, including a Convolutional Neural Network (CNN)-based model with MobileNetV2 and a Support Vector Machine (SVM) baseline for comparison. The system's performance was validated on a dataset of 106 footprint impressions collected from 53 healthy young university students. The training set was subsequently augmented to 300 images, while evaluation was performed on 22 held-out original (non-augmented) images. The total accuracy for the CNN model was 77.3%, with an F1-score by class of 0.78 for Pes Cavus, 0.92 for Pes Planus, and 0.62 for Normal Arch. Precision values were 0.70 (Pes Cavus), 0.86 (Pes Planus), and 0.80 (Normal Arch), and recall values were 0.88, 1.00, and 0.50, respectively. The SVM model had an accuracy of 54.6%. Cohen's Kappa of 0.658 indicated substantial agreement between model predictions and reference labels, and ROC analysis showed promising classification performance within the evaluated dataset, with AUC estimates of 0.848 for Pes Cavus, 0.990 for Pes Planus, and 0.830 for Normal Arch.