## 2 Methods
### Feature Description and Data Structure  
For the first set of experiments, which focused on predicting plantar pressure metrics using temperature data, each data point consisted of temperature measurements recorded at five specific time intervals immediately after walking (0 s) and at 30 s, 90 s, 120 s, and 180 s. The data were collected from eight anatomical points on each foot: the hallux, first metatarsus, third metatarsus, fifth metatarsus, heel, lateral midfoot (LatMF), and medial midfoot (MedMF). Each anatomical point contributed temperature features based on three summary statistics (mean, maximum, and minimum) in addition to the five time-specific measurements. This resulted in 15 temperature features per anatomical point (5 time-based measurements + 3 summary statistics). Given that temperature data were collected from 8 anatomical points, each foot provided a total of 120 temperature features (15 features \(\times\) 8 anatomical points). These 120 temperature features were used as input to the machine learning regression models, with the target being one of the three plantar pressure metrics: peak pressure (PPP), average peak pressure (APPP), or average pressure (APP) for each anatomical point. The models tested included Extra Trees Regressor, K Neighbors Regressor, Dummy Regressor, Light Gradient Boosting Machine, Bayesian Ridge, Random Forest Regressor, Gradient Boosting Regressor, AdaBoost Regressor, Extreme Gradient Boosting, Orthogonal Matching Pursuit, Elastic Net, Lasso Least Angle Regression, Lasso Regression, Ridge Regression, Decision Tree Regressor, Huber Regressor, Linear Regression, Passive Aggressive Regressor, and Least Angle Regression. The goal of these regression models was to assess whether the temperature data alone could be used to predict the corresponding plantar pressure values. However, as detailed in the results, the models faced difficulties due to the low or insignificant correlations between temperature and pressure variables. In the subsequent experiments, a multimodal approach was employed for the classification task of predicting diabetic status. In this setup, both temperature and pressure data were combined into the feature set. Pressure data were summarized into 3 metrics for each of the seven anatomical points (first metatarsus, third metatarsus, fifth metatarsus, hallux, heel, lateral midfoot, and medial midfoot), resulting in 21 pressure-related features (3 metrics \(\times\) 7 points). When combined with the 120 temperature features, the complete feature vector used for classification consisted of 141 attributes per foot (120 temperature features + 21 pressure features). These feature vectors were used in machine learning classification models aimed at distinguishing between diabetic and non-diabetic subjects. The models tested in this task included Extra Trees Classifier, Random Forest Classifier, Extreme Gradient Boosting, Ada Boost Classifier, Gradient Boosting Classifier, Naive Bayes, Logistic Regression, Decision Tree Classifier, Linear Discriminant Analysis, Ridge Classifier, Quadratic Discriminant Analysis, K Neighbors Classifier, Support Vector Machine (SVM) with a linear kernel, Light Gradient Boosting Machine, and Dummy Classifier. Figure 3: Regions of interest on the feet were marked for thermal and pressure measurements: hallux, 1 st metatarsus, 3 rd metatarsus, 5 th metatarsus, midfoot (proximal to 5 th metatarsus apophysis), medial arch on proximal 1 st metatarsus, and heel. The models were implemented and evaluated using the PyCaret library [36], which automates the training and evaluation of machine learning algorithms for both regression and classification tasks. A 5-fold cross-validation strategy was employed to evaluate model performance. The dataset comprised 26 participants, with each foot treated as an independent instance, resulting in 52 instances in total.