Non-Invasive Core Body Temperature Estimation Using Dynamic Thermal Model
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Solution Overview
Problem
Conventional methods for non-invasively estimating core body temperature in living beings face errors due to transient changes in thermal resistance and blood flow, requiring frequent calibration of proportional coefficients, especially when the subject is in motion or exposed to varying environments.
Innovation Solution
A temperature estimation system employing multiple sensors and a machine learning approach to estimate the proportional coefficient based on surface and internal temperature measurements, as well as heart rate data, allowing for accurate internal temperature calculation without continuous calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional thermal equivalent circuit model is used to estimate core body temperature, then non-invasive measurement is achieved, but transient errors occur when living body is exposed to wind, runs, or moves between temperature zones
Solution Approach 1:
The patent divides the thermal measurement system into multiple segments: skin surface temperature sensors, sensor package temperature sensors, and ambient temperature sensors. Each segment measures different thermal parameters, and their combined data provides accurate core body temperature estimation while maintaining non-invasive operation.
Solution Approach 2:
The system continuously monitors skin surface temperature, sensor package temperature, and ambient temperature, then feeds this information back to dynamically adjust the thermal model parameters. This feedback mechanism compensates for transient changes in blood flow and thermal resistance, eliminating estimation errors during motion or environmental changes.
2Device complexity
If conventional method assumes constant thermal resistance and proportional coefficient, then calculation is simplified, but estimation accuracy deteriorates when blood flow varies with posture or exercise
Solution Approach 1:
The patent transforms the static thermal model into a dynamic one by continuously updating thermal resistance and proportional coefficient based on real-time measurements of skin temperature, sensor package temperature, and ambient temperature. This dynamic adaptation maintains calculation feasibility while accurately reflecting blood flow variations during exercise or posture changes.
Solution Approach 2:
The system dynamically changes thermal model parameters (thermal resistance Rbody and proportional coefficient α) based on measured temperature differences and ambient conditions. Instead of using fixed constants, the parameters are continuously adjusted to match physiological states, resolving the contradiction between model simplicity and accuracy.
3Measurement precision
If calibration of proportional coefficient is performed using eardrum thermometer, then measurement accuracy is improved, but measurement process becomes more complex and time-consuming
Solution Approach 1:
The system performs self-calibration by using the measured skin surface temperature, sensor package temperature, and ambient temperature to automatically determine the proportional coefficient without requiring external calibration devices. The thermal model uses these measurements to self-adjust parameters, eliminating the need for eardrum thermometer calibration while maintaining accuracy.
Solution Approach 2:
The patent extracts the calibration function from the measurement process by deriving the proportional coefficient directly from ambient temperature and temperature differential measurements. This separates the calibration requirement from invasive eardrum measurement, allowing accurate calibration through non-invasive temperature sensors alone.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method reduces estimation errors and provides accurate core body temperature readings without the need for frequent calibration, even during dynamic conditions or environmental changes.
Implementation Method 1
a first temperature sensor provided on a surface of the heat insulation material facing the test object and configured to measure a temperature of a surface of the test object
Implementation Method 2
a second temperature sensor configured to measure a temperature inside the heat insulation material immediately above the first temperature sensor
Implementation Method 3
a heat insulation material
Data Source
AI summary
A temperature estimation system includes a temperature sensor for measuring a temperature of a surface of a living body, a temperature sensor for measuring a temperature inside a heat insulation material, a temperature sensor for measuring a temperature of the surface of the living body at a position remote from the temperature sensor, a learner for estimating a proportional coefficient associated with a thermal resistance of the living body on the basis of measurement results of the temperature sensors, and a temperature calculation unit for calculating a core body temperature of the living body on the basis of measurement results of the temperature sensors and the proportional coefficient