Nonlinear Curve Fitting for Rapid Body Temperature Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional thermometers, including electronic predictive thermometers, face challenges in accurately and rapidly measuring body temperature due to slow heat transfer and complex thermodynamic interactions between the thermometer and body tissue, leading to inaccuracies and the need for prolonged measurement times, especially when measuring at different anatomical sites like oral, rectal, and axillary regions.
Innovation Solution
A thermometer system that employs a nonlinear model with multiple parameters to fit a temperature curve to monitored data, allowing for early prediction of stabilization temperature, adapting to the thermal characteristics of the probe and anatomy, and using a processor to select appropriate parameters for accurate predictions with minimal data acquisition and processing time, enabling measurement at multiple sites with a single device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional glass bulb thermometer or electronic temperature probe is used to measure body temperature, then the temperature measurement is relatively accurate, but the measurement time is prolonged (2-8 minutes for glass thermometer, 3-5 minutes for electronic probe)
Solution Approach 1:
The system performs preliminary temperature sampling at multiple rates during the measurement process, collecting data points before the sensor reaches thermal equilibrium. This preliminary data collection enables the prediction algorithm to calculate the final equilibrium temperature without requiring the full stabilization time, thus reducing measurement time while maintaining accuracy
Solution Approach 2:
The system continuously monitors the temperature signal and uses feedback from multiple sampling rates to adjust the prediction calculation. By analyzing the temperature change rate and comparing measurements from different sampling rates, the system can determine when sufficient data has been collected to make an accurate prediction, optimizing both speed and precision
2Speed
If an electronic temperature probe is used to reduce measurement time, then the measurement is faster, but the temperature reading lags behind the actual body temperature due to heat transfer resistance
Solution Approach 1:
The system introduces an algorithmic intermediary that processes the raw temperature data from the probe. Instead of directly displaying the lagging probe temperature, the system uses prediction algorithms that calculate what the equilibrium temperature will be based on the observed temperature change pattern, effectively compensating for the thermal lag
Solution Approach 2:
The system performs preliminary analysis of the temperature signal characteristics during the measurement process, calculating the expected equilibrium temperature before the probe actually reaches equilibrium. This allows the system to display the predicted final temperature while the probe is still warming up, eliminating the perceived lag
3Loss of time
If prediction algorithms are used to estimate temperature before thermal stabilization, then the measurement time is reduced, but the prediction accuracy may decline unless complex processing is performed
Solution Approach 1:
The system segments the temperature measurement process into multiple sampling phases with different sampling rates. By collecting data at high initial sampling rates and then transitioning to lower rates, the system gathers sufficient information for accurate prediction without requiring continuous high-rate sampling, reducing overall processing time while maintaining prediction accuracy
Solution Approach 2:
The system dynamically changes sampling parameters during the measurement process, adjusting the sampling rate based on the temperature change rate. When the temperature is changing rapidly, higher sampling rates are used to capture the dynamics; when approaching equilibrium, lower rates suffice. This adaptive parameter adjustment optimizes both speed and accuracy
4Measurement precision
If multiple temperature sampling rates are used to improve prediction accuracy, then the temperature prediction is more precise, but the data processing complexity increases
Solution Approach 1:
The system uses multiple sampling rates but processes only the essential data needed for prediction. By collecting more data than strictly necessary at different rates and then using algorithms that focus on the most informative portions, the system achieves high prediction accuracy without requiring complex processing of all collected data points
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 approach allows for rapid, accurate temperature prediction at an early stage of the measurement process, reducing the need for prolonged measurement times and accommodating varying measurement conditions, while using simple and cost-effective circuitry, thus providing timely and precise diagnostic information across multiple anatomical sites.
Implementation Method 1
heat flow from the surface of the body to the temperature sensor
Data Source
AI summary
A thermometer system and method that rapidly predict body temperature based on the temperature signals received from a temperature sensing probe when it comes into contact with the body. A nonlinear, multi-parameter curve fitting process is performed and depending on the errors in the curve fit, parameters are changed or a prediction of the temperature is made. Criteria exist for the differences between the curve fit and the temperature data. The processor switches to a Continuous Monitor State if the curve fit over a limited number of time frames is unacceptable. Determining the start time on which the measurement time frame for prediction is based is performed by tissue contact threshold coupled with a prediction time delay.