Body Temperature Prediction System Using Context-Aware Machine Learning

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Solution Overview

Problem

Conventional prediction systems fail to accurately inform users about future body temperature changes based on their activity schedules, limiting their ability to manage heat-related health issues effectively.

Innovation Solution

A prediction system that learns the relationship between body temperature and context using machine learning, allowing it to predict future body temperature changes for users based on their scheduled activities and provide this information to them.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional prediction systems use learning models to predict heat stroke risk based on physical condition and environmental data, then heat stroke risk prediction capability is improved, but the ability to inform users about specific future body temperature changes deteriorates

Engineering Contradiction:

Improveheat stroke risk prediction capabilityVSAvoidbody temperature change information

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The prediction system segments the overall heat stroke risk prediction into two distinct components: (1) a learning model that predicts future body temperature based on physical condition and environmental data, and (2) a heat stroke risk determination unit that evaluates the predicted temperature against safety thresholds. This segmentation allows the system to provide both specific body temperature change information and heat stroke risk assessment, resolving the contradiction between predicting risk and informing about temperature changes.

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If the system calculates prediction values based on past measurement data trends, then future body temperature prediction is improved, but user understanding of specific temperature changes deteriorates

Engineering Contradiction:

Improvefuture body temperature prediction accuracyVSAvoiduser understanding of temperature changes

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system introduces an intermediate processing unit that translates complex prediction values derived from past trends into user-friendly temperature change information. The learning model calculates prediction values based on historical data trends, then the body temperature information output unit converts these into interpretable formats showing specific temperature changes users can understand and act upon, bridging the gap between accurate prediction and user comprehension.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20230059310A1Prediction system, prediction device, prediction method, and non-transitory computer readable storage

Publication Date: 2023.02.23 JAPAN COMP VISION CORP

AI summary

A prediction system according to the present application includes an acquisition unit and a prediction unit. The acquisition unit acquires body temperature information indicating a body temperature of a first user and first context information indicating a first context which is a context when the body temperature of the first user is detected. The prediction unit predicts information regarding a body temperature change of a second user in the future, on the basis of a learned model that has learned a relation between the body temperature and the first context and second context information indicating a second context that is a future context of the second user.