Earticle

초록

영어

This study presents a deep-learning-based framework integrating a single heat-flux sensor for rapid and accurate estimation of core body temperature (CBT). A gated recurrent unit (GRU) model was trained using a large-scale dataset comprising finite-element simulations and experimental transient responses to learn conduction- and convection-governed features. By analyzing only the initial 30 s of temperature signals, the model achieved a mean absolute error below 0.1 °C across a wide range of ambient temperatures (5–35 °C), convective heat transfer coefficients (0–50 W·m⁻²·K⁻¹), and skin conductivities (0.32–0.50 W·m⁻¹·K⁻¹). This data-driven approach eliminated the need for prolonged thermal stabilization, enabling site-independent CBT prediction with reliable performance under dynamic environmental and physiological conditions.

목차

Abstract
I. INTRODUCTION
II. METHOD
A. Finite element method simulation
B. Data-driven neural network
III. RESULTS AND DISCUSSION
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Han Kyung Kim [ Department of Electrical and Computer Engineering Inha University Incheon, South Korea ]
• Jong Moon Kim [ Department of Electrical and Computer Engineering Inha University Incheon, South Korea ]
• Dae Yu Kim [ Department of Electrical and Computer Engineering Inha University Incheon, South Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004