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

arXiv AI Archived Mar 16, 2026 ✓ Full text saved

arXiv:2603.12278v1 Announce Type: cross 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 mach

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    Quantitative Biology > Other Quantitative Biology [Submitted on 27 Feb 2026] Predictive Analytics for Foot Ulcers Using Time-Series Temperature and Pressure Data Md Tanvir Hasan Turja 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. Comments: 36 pages, 19 figures Subjects: Other Quantitative Biology (q-bio.OT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.12278 [q-bio.OT]   (or arXiv:2603.12278v1 [q-bio.OT] for this version)   https://doi.org/10.48550/arXiv.2603.12278 Focus to learn more Submission history From: Md Tanvir Hasan Turja [view email] [v1] Fri, 27 Feb 2026 17:30:27 UTC (2,699 KB) Access Paper: HTML (experimental) view license Current browse context: q-bio.OT < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.AI cs.LG q-bio References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
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    Mar 16, 2026
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