Thermal Sensor Biometric Data Extraction
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Solution Overview
Problem
Current image sensors, including thermal sensors, lack the capability to effectively expand their applications for preventive measures in scenarios like health condition monitoring and hazard detection in smart home environments, particularly failing to provide timely and accurate biometric data extraction and analysis.
Innovation Solution
Enhancing thermal sensor performance by increasing resolution, frame rate, and sensitivity, and employing signal processing techniques and deep learning models for biometric data extraction and hazard prediction, with the ability to adapt settings based on individual thermal signatures and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If thermal sensor parameters (resolution, frame rate, sensitivity) are increased to improve biometric data extraction capability, then measurement precision and reliability are improved, but device complexity and cost increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting thermal sensor parameters (resolution, frame rate, sensitivity) based on detected conditions and individual thermal signatures. The system enhances measurement precision by increasing these parameters when needed for accurate biometric data extraction, while avoiding constant high-parameter operation to manage device complexity. This selective parameter enhancement resolves the contradiction by optimizing the balance between measurement accuracy and system complexity.
2Reliability
If deep learning models and signal processing techniques are implemented to improve hazard detection accuracy, then measurement precision is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent employs signal processing techniques as intermediary steps between raw thermal sensor data and deep learning model analysis. These intermediate processing stages (noise filtering, feature extraction, thermal signature analysis) simplify the data before it reaches the complex deep learning models, thereby improving hazard detection reliability while managing the overall processing system complexity by breaking down the analysis into manageable stages.
Solution Approach 2:
The system performs preliminary actions by pre-processing thermal images and extracting thermal signatures before applying deep learning models for hazard detection. This preliminary analysis prepares the data in advance, allowing the complex models to operate more efficiently and accurately, thus improving reliability while controlling the complexity burden on the main processing system.
3Measurement precision
If the thermal sensor operates at high frame rates and resolution continuously, then biometric data extraction accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamics by making the thermal sensor operation adaptive rather than static. The system dynamically adjusts frame rate and resolution based on detected conditions, such as presence of individuals, detected hazards, or specific monitoring needs. This dynamic operation maintains high measurement precision when required while reducing energy consumption during normal or low-activity periods, effectively resolving the contradiction between image quality and power usage.
4Reliability
If the system stores and processes large amounts of thermal image data for analysis, then hazard detection accuracy is improved, but loss of time for data processing increases
Solution Approach 1:
The patent extracts only the essential and relevant features from thermal images for hazard detection and biometric analysis, rather than processing complete high-resolution images. By extracting thermal signatures, temperature patterns, and key biometric features, the system maintains high hazard detection accuracy while significantly reducing data processing time and computational burden, thus resolving the contradiction between reliability and processing speed.
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
Enables early warning systems for health conditions and hazard detection, providing personalized smart home applications that adapt to individual preferences and improve safety by accurately extracting and analyzing biometric data from thermal images.
Implementation Method 1
a thermal sensor for obtaining sensor information
Data Source
AI summary
A health condition of a person may be assessed from a thermal sensor signal. By increasing performance indices of a thermal camera (for example, resolution, frame rate, sensitivity), operation may be extended to identification verification, biometric data extraction and health condition analysis, and so forth. Prediction may be carried out by monitoring a time sequence of thermal images, and consequently early warning of the health condition may be provided. The apparatus may be used for, but not limited to, personalization of smart home devices through supervised and reinforcement learnings. The application of the apparatus may be, but not limited to, smart homes, smart buildings and smart vehicles, and so forth.