Personalized Cold Stress Prediction With Adaptive Thermoregulation Models
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Solution Overview
Problem
Current thermoregulation models fail to accurately predict cold stress risk at the individual level due to significant differences between individuals, especially in extreme cold environments, leading to invalid and non-specific prediction results.
Innovation Solution
A method using deep learning to iteratively adjust key parameters of a thermoregulation model based on personalized features and environmental variables, including data cleaning, formatting, and genetic algorithms to optimize metabolic rates and heat capacities for individual segments of the body.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If population-level thermoregulation models are used, then the model structure is simple and easy to implement, but the prediction accuracy for individual cold stress risk is low
Solution Approach 1:
The patent applies parameter changes by adjusting key thermoregulation model parameters (metabolic rate, set-point temperature, heat capacity, thermal conductivity) from population-level fixed values to individualized values based on personal characteristics (age, gender, body composition) and real-time physiological data. This allows the model to adapt to individual differences while maintaining the same model structure, thereby improving prediction accuracy without significantly increasing model complexity.
Solution Approach 2:
The patent implements dynamics by transitioning from static population-level parameters to dynamic individualized parameters that can be adjusted in real-time based on physiological responses and environmental conditions. The model dynamically updates key parameters during cold exposure to reflect individual variations in thermoregulation, enabling accurate personalized prediction while keeping the computational framework manageable.
2Measurement precision
If personalized factors are incorporated into thermoregulation models, then individual-level prediction accuracy improves to some extent, but the models still cannot achieve accurate prediction due to significant differences between individuals
Solution Approach 1:
The patent applies local quality by differentiating parameter adjustment strategies for different body segments and individual characteristics. Instead of applying uniform adjustments across all individuals, the model tailors key parameter modifications (metabolic rate, heat capacity, thermal conductivity) to specific personal attributes and segmental differences, thereby achieving accurate prediction across diverse individuals while maintaining model adaptability.
Solution Approach 2:
The patent implements segmentation by dividing the body into multiple thermal segments and applying individualized parameter adjustments to each segment based on local tissue properties and physiological characteristics. This segmented approach allows the model to capture inter-individual variations in different body regions, improving overall prediction accuracy while maintaining adaptability to diverse individual profiles.
3Measurement precision
If deep learning algorithms are used to iteratively adjust model parameters, then personalized prediction accuracy is significantly improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and formatting physiological and environmental data before feeding it to the deep learning algorithm. The system performs data cleaning, feature extraction, and initial parameter estimation in advance, which reduces the computational burden during iterative parameter adjustment and enables personalized prediction without overwhelming computational complexity.
Solution Approach 2:
The patent implements feedback by using the deep learning algorithm to iteratively adjust model parameters based on the difference between predicted and actual physiological responses. The system continuously refines key parameters (metabolic rate, heat capacity, thermal conductivity) through feedback loops, achieving high personalized prediction accuracy while managing computational complexity through efficient feedback mechanisms and iterative optimization.
Data Source
AI summary
A method and a system for predicting and evaluating personalized cold stress risk based on deep learning are provided. The method includes sequentially performing data cleaning and formatting on received feature parameters of a subject and environmental variables at a location of the subject; according to a prebuilt thermoregulation model, performing a preliminary prediction on a skin temperature of the subject, and determining a target segment based on a result of the preliminary prediction and formatted feature parameters including one or more of metabolic rate, set-point temperature, or heat capacity; based on a deep learning algorithm, iteratively adjusting the key parameter for the target segment according to the formatted data; and according to the thermoregulation model with adjusted key parameters, performing a secondary prediction on the skin temperature, determining a cold stress risk of the subject based on the result of secondary prediction, and feeding it back to the subject.