Multimodal sensing of thermal comfort for adaptable climate control
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Solution Overview
Problem
Traditional climate control systems in vehicles and buildings rely on static environmental settings, leading to increased energy consumption and inconsistent thermal comfort due to the inability to automatically detect and respond to individual thermal discomfort levels, which are influenced by personal and environmental factors.
Innovation Solution
An adaptable climate control method using thermal imaging and physiological data to identify thermal features, construct thermal feature vectors, and classify occupant comfort states, allowing for automatic adjustments to maintain thermal comfort while reducing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static environmental condition control is used to maintain the driver's space in a selected state, then the system is simple to operate, but energy consumption increases and thermal comfort cannot be permanently ensured
Solution Approach 1:
The system enables automatic thermal comfort detection and climate control adjustment without requiring manual user intervention. The multimodal sensing system continuously monitors physiological signals (skin temperature, heart rate variability) and environmental conditions, then automatically adjusts climate control parameters to maintain optimal thermal comfort, allowing the system to serve itself rather than requiring constant user operation.
Solution Approach 2:
The system implements continuous feedback loops by monitoring physiological signals and environmental conditions through multiple sensors, processing this data through machine learning algorithms to detect thermal discomfort states, and automatically adjusting climate control settings in response. This closed-loop feedback mechanism ensures energy-efficient operation while maintaining thermal comfort without requiring manual user input.
2Reliability
If manual temperature adjustments are made to maintain thermal comfort, then energy consumption increases, but the system cannot ensure permanent thermal comfort sensation
Solution Approach 1:
The system continuously and automatically monitors physiological signals (skin temperature, heart rate variability) and environmental conditions to detect thermal discomfort states without requiring manual user input. The machine learning algorithms process this data in real-time and automatically adjust climate control parameters, enabling the system to self-regulate and maintain consistent thermal comfort while minimizing energy consumption through precise, need-based adjustments.
Solution Approach 2:
The system implements continuous feedback loops by monitoring physiological signals and environmental conditions through multiple sensors, processing this data through machine learning algorithms to detect thermal discomfort states, and automatically adjusting climate control settings in response. This closed-loop feedback mechanism ensures energy-efficient operation while maintaining thermal comfort without requiring manual user input.
3Device complexity
If traditional single-mode sensing is used for thermal discomfort detection, then the system is simpler, but detection accuracy and effectiveness are reduced
Solution Approach 1:
The system merges multiple sensing modalities including thermal cameras for skin temperature mapping, physiological sensors for heart rate variability and skin conductance monitoring, and environmental sensors for temperature and humidity detection. These diverse sensor inputs are integrated and processed through machine learning algorithms to comprehensively detect thermal discomfort states, achieving high detection accuracy by combining complementary information from multiple sources rather than relying on a single sensor type.
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 effectively detects thermal discomfort and adjusts the climate to maintain occupant comfort, reducing energy consumption by integrating thermal and physiological data for personalized climate control in vehicles and buildings.
Implementation Method 1
receiving, by a computer processor, a thermal image of a target area of the occupant
Data Source
AI summary
An adaptable climate control method for controlling a thermal climate within a confined space based on thermal and/or physiological features of one or more occupants of the confined space is provided. The method includes receiving thermal images of a plurality of target areas from the occupants, identifying a plurality of interesting points of the received thermal images, and isolating the interesting points to construct thermal maps of the target areas. Thermal features of the occupants are determined using the respective thermal maps and the thermal features are used to construct thermal feature vectors for the occupants. Physiological features from physiological sensors can also be added as elements of the thermal feature vectors to form integrated vectors. The thermal feature vectors and/or integrated vectors are classified using a classifiers and at least a portion of the thermal climate of the confined space is adjusted based on the classification of the vectors.