An investigation of foot temperature deviations in individuals with diabetes : insights from wearable in-shoe technology

Abstract

Plantar foot temperature is a valuable indicator of diabetes-related complications, but traditional assessment methods, such as infrared thermography and contact thermometers, require unshod feet and controlled conditions, limiting their practicality for continuous monitoring. In this study, we employ a smart insole with 21 embedded temperature sensors to capture plantar temperature data from shod feet. We introduce a novel approach that leverages per-foot relative temperature values—normalized to the foot’s mean—rather than absolute values or inter-foot asymmetry. Using data collected during static postures (lying, sitting, and standing), we evaluate multiple machine learning classifiers, with Random Forest achieving the highest accuracy (83.20%), alongside high sensitivity (93.75%) but moderate specificity (63.6%). To enhance explainability, we apply SHAP analysis to interpret model predictions and identify key sensor contributions. Additionally, we derive simple decision rules from the Random Forest model, finding that two medial arch sensors can achieve near-equivalent accuracy (80.38% and 79.82%) to the full model. These results suggest that deviations in plantar temperature patterns could serve as an indicator of diabetes-related foot health changes. Future work will expand this approach to ambulatory activities, integrating static and dynamic features to develop an insole-based system for continuous foot health monitoring in real-world settings.