Biometeorological Sensor with Computer Vision for MRT
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Solution Overview
Problem
Current biometeorological sensing devices are expensive, bulky, and lack vision capabilities, making them inadequate for accurately measuring Mean Radiant Temperature (MRT) and monitoring pedestrian behavior in public spaces, especially in hot, dry climates, where shade management is crucial for human comfort.
Innovation Solution
A low-cost, portable IoT weather station (MaRTiny) that combines meteorological sensors with computer vision, using a processor, humidity, UV, anemometer, and camera to estimate MRT, detect people in shade and sun, and transmit data to a server, employing machine learning models like Bi-directional Feature Pyramid with Recurrent Attention Residual Module for shade detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive equipment like MaRTy is used for MRT measurement, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a gray globe thermometer as a simplified copy of the complex radiometric measurement system. The globe thermometer replicates the essential function of measuring radiant temperature exposure by using a standardized gray sphere with known thermal properties, providing adequate MRT measurement precision without requiring expensive radiometric sensors and complex data processing systems.
Solution Approach 2:
The patent employs low-cost, readily available sensors including a gray globe thermometer, humidity sensor, UV sensor, anemometer, and atmospheric thermometer. These inexpensive components replace expensive specialized equipment while maintaining sufficient measurement accuracy for biometeorological applications in hot climates.
2Productivity
If computer vision is added to detect pedestrians and shade, then space use monitoring is improved, but device complexity increases
Solution Approach 1:
The camera serves multiple functions: it captures images for pedestrian detection and counting, identifies shade areas through image analysis, and can potentially monitor environmental conditions. This multi-functionality improves space use monitoring capabilities without proportionally increasing device complexity, as a single camera module performs several monitoring tasks.
Solution Approach 2:
The system uses the camera to automatically detect and count pedestrians and identify shade areas without requiring manual observation or additional specialized sensors. The image processing algorithms enable the system to self-monitor space usage patterns and thermal comfort conditions autonomously.
3Adaptability or versatility
If multiple sensors are integrated for comprehensive monitoring, then measurement capability is improved, but ease of operation deteriorates
Solution Approach 1:
The patent integrates multiple sensors (globe thermometer, humidity sensor, UV sensor, anemometer, atmospheric thermometer, and camera) into a single portable weather station unit. This consolidation provides comprehensive monitoring of meteorological conditions, heat exposure, and space use while maintaining ease of operation through unified power supply, synchronized data collection, and integrated communication systems.
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
MaRTiny provides accurate, low-cost MRT measurements and pedestrian counting with high precision (95%) and shade detection accuracy (80%), reducing the need for human labor and expensive equipment, while correlating heat exposure with space use patterns.
Implementation Method 1
A more human-centric metric that emphasizes the heat load on the human body is the Mean Radiant Temperature (hereinafter, MRT). MRT objectively quantifies the total short- and longwave radiation the human body is exposed to
Implementation Method 2
generate a shadow map from the image; calculate an intersection over union (IOU) of the bounding box with the shadow map to determine if the person is in the shade
Data Source
AI summary
The disclosure concerns biometeorological sensing devices including a processor communicatively coupled to a memory, and a plurality of sensors communicatively coupled to the processor. The plurality of sensors includes a humidity sensor, a UV sensor, an anemometer, an atmospheric thermometer, a globe thermometer, and a camera. The device also includes a network interface communicatively coupled to the processor. The processor is configured to estimate a mean radiant temperature (MRT) using data received from the plurality of sensors, identify a person in an image received from the camera, determine a bounding box that encloses the person in the image, generate a shadow map from the image, calculate an intersection over union (IOU) of the bounding box with the shadow map to determine if the person is in the shade, and transmit observed space usage and estimated MRT to a server communicatively coupled to the network interface.