Human Body Surface Temperature Calculation via Thermal Image Segmentation
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Solution Overview
Problem
Existing technologies struggle to accurately calculate the body surface temperature of a person in a target space, especially when influenced by nearby heat sources.
Innovation Solution
A human body surface temperature calculation system that uses an infrared sensor to obtain temperature distribution data, generates a thermal image, employs a machine learning model to extract the human region, and calculates the body surface temperature by isolating temperature values corresponding to the human region from other heat sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temperature distribution data from infrared sensor is used to calculate body surface temperature, then temperature measurement capability is provided, but measurement precision deteriorates due to influence from nearby heat sources
Solution Approach 1:
The patent segments the temperature distribution data by identifying and separating the human body region from the overall temperature field using machine learning models. This segmentation allows extraction of temperature values specifically from the human body region, isolating them from interfering heat sources in the surrounding environment.
Solution Approach 2:
The patent extracts the human body region and its corresponding temperature values from the composite temperature distribution data. By using machine learning to identify and extract only the relevant temperature data from the human body, the system removes the influence of nearby heat sources that are not part of the target measurement.
2Measurement precision
If machine learning model is used to extract human region from thermal image, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical or rule-based region extraction methods with a machine learning model. This substitution enables more accurate identification of the human body region in thermal images, handling complex scenarios that would be difficult to address with conventional algorithms.
Solution Approach 2:
The machine learning model acts as an intermediary between the raw thermal image data and the final temperature calculation. It processes the thermal image to generate a segmented human region mask, which then serves as the basis for accurate temperature extraction, bridging the gap between raw data and meaningful measurement.
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
The system accurately calculates the body surface temperature of a person in a target space, effectively isolating and accounting for nearby heat sources, thereby providing precise temperature readings.
Implementation Method 1
an infrared sensor; an obtainer that obtains temperature distribution data indicating a temperature distribution in a target space, the temperature distribution data being obtained by the infrared sensor
Data Source
AI summary
A human body surface temperature calculation system includes: an infrared sensor; an obtainer that obtains temperature distribution data indicating a temperature distribution in a target space which is obtained by the infrared sensor; a thermal image generator that generates a thermal image of the target space based on the temperature distribution data obtained by the obtainer; an extractor that extracts, using a machine learning model, a human region indicating a person captured in the thermal image; and a calculator that extracts a temperature value group corresponding to the human region from the temperature distribution data and calculates a body surface temperature of the person based on the temperature value group extracted.