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Predictive Analytics for Foot Ulcers Using Time-Series Temperature and Pressure Data

Md Tanvir Hasan Turja

Abstract

Diabetic foot ulcers (DFUs) are a severe complication of diabetes, often resulting in significant morbidity. This paper presents a predictive analytics framework utilizing time-series data captured by wearable foot sensors -- specifically NTC thin-film thermocouples for temperature measurement and FlexiForce pressure sensors for plantar load monitoring. Data was collected from healthy subjects walking on an instrumented pathway. Unsupervised machine learning algorithms, Isolation Forest and K-Nearest Neighbors (KNN), were applied to detect anomalies that may indicate early ulcer risk. Through rigorous data preprocessing and targeted feature engineering, physiologic patterns were extracted to identify subtle changes in foot temperature and pressure. Results demonstrate Isolation Forest is sensitive to micro-anomalies, while KNN is effective in flagging extreme deviations, albeit at a higher false-positive rate. Strong correlations between temperature and pressure readings support combined sensor monitoring for improved predictive accuracy. These findings provide a basis for real-time diabetic foot health surveillance, aiming to facilitate earlier intervention and reduce DFU incidence.

Predictive Analytics for Foot Ulcers Using Time-Series Temperature and Pressure Data

Abstract

Diabetic foot ulcers (DFUs) are a severe complication of diabetes, often resulting in significant morbidity. This paper presents a predictive analytics framework utilizing time-series data captured by wearable foot sensors -- specifically NTC thin-film thermocouples for temperature measurement and FlexiForce pressure sensors for plantar load monitoring. Data was collected from healthy subjects walking on an instrumented pathway. Unsupervised machine learning algorithms, Isolation Forest and K-Nearest Neighbors (KNN), were applied to detect anomalies that may indicate early ulcer risk. Through rigorous data preprocessing and targeted feature engineering, physiologic patterns were extracted to identify subtle changes in foot temperature and pressure. Results demonstrate Isolation Forest is sensitive to micro-anomalies, while KNN is effective in flagging extreme deviations, albeit at a higher false-positive rate. Strong correlations between temperature and pressure readings support combined sensor monitoring for improved predictive accuracy. These findings provide a basis for real-time diabetic foot health surveillance, aiming to facilitate earlier intervention and reduce DFU incidence.
Paper Structure (37 sections, 19 figures)

This paper contains 37 sections, 19 figures.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. 1. Data Collection and Understanding
  5. 2. Data Pre-Processing Techniques
  6. 3. Analysis Framework
  7. 3.1 Pressure-Based Analysis
  8. Activity Window Identification and Truncation
  9. Visualization of Selected Patient IDs Based on Pressure Data
  10. Features
  11. Distribution of features across pData(Pressure Data) types
  12. Anomaly Detection Using Isolation Forest
  13. Feature Importance for Pressure Anomaly Detection for Isolation Forest
  14. Visualization of Isolation Forest Results
  15. Concentration of Anomalies for some most important features
  16. ...and 22 more sections

Figures (19)

  • Figure 1: Visualization of Selected Patient IDs Based on Pressure Data
  • Figure 2: Distribution of features across pData(Pressure Data) types
  • Figure 3: Feature Importance for Pressure Anomaly Detection for Isolation Forest
  • Figure 4: Pressure Data (Anomaly Scores for Pressure Data)
  • Figure 5: Concentration of Anomalies for some most important features (Pressure)
  • ...and 14 more figures