Camera-Based Breathing Monitoring for Low-Cost CO2 Estimation
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Solution Overview
Problem
Existing CO2 monitoring technologies are costly and limited to single-function CO2 level measurement, failing to effectively address the critical need for monitoring and mitigating increased CO2 levels in confined spaces, such as vehicles, where multiple factors influence respiration rates.
Innovation Solution
A device and system that utilizes image data from cameras to monitor breathing parameters, estimating CO2 levels based on changes in breathing rate and depth, and generates output signals for controlling ventilation or recommending actions to normalize CO2 levels, incorporating breathing monitors and CO2 estimation units to provide multi-purpose, cost-effective monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CO2 sensors (NDIR sensors) are used to monitor CO2 concentration level, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent uses breathing parameters (breathing rate, breathing depth) as an intermediary to indirectly estimate CO2 levels. Instead of directly measuring CO2 concentration with expensive NDIR sensors, the system monitors breathing patterns which correlate with CO2 levels, using these parameters as a mediator to infer environmental CO2 conditions.
Solution Approach 2:
The patent replaces the mechanical/optical sensing system (NDIR sensors) with a computational approach using image processing and machine learning algorithms. The system substitutes physical CO2 detection hardware with software-based analysis of breathing patterns captured by standard cameras, eliminating the need for specialized sensing hardware.
2Measurement precision
If CO2 sensors are used for CO2 monitoring, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent makes standard cameras perform multiple functions: capturing visual images and simultaneously monitoring breathing parameters. The same imaging hardware used for general surveillance or recording is also utilized for CO2-related monitoring, eliminating the need for dedicated specialized sensors and reducing overall system complexity.
Solution Approach 2:
The patent replaces complex physical sensing mechanisms with software-based analysis. Instead of requiring specialized NDIR sensor hardware and associated signal processing circuits, the system uses standard image processing techniques and machine learning algorithms to extract breathing information from video feeds, significantly simplifying the hardware architecture.
3Ease of manufacture
If breathing parameters are monitored to estimate CO2 levels, then device cost is reduced, but measurement precision deteriorates
Solution Approach 1:
The system continuously monitors breathing parameters and uses this feedback to dynamically adjust and refine CO2 level estimates. By continuously tracking changes in breathing rate and depth over time, the system can detect trends and patterns that improve the accuracy of CO2 estimation, allowing the estimates to become more precise as more data is collected.
Solution Approach 2:
The system performs preliminary monitoring of breathing parameters to establish baseline values before making CO2 level estimates. By first collecting and analyzing breathing pattern data to understand individual respiratory characteristics, the system can then more accurately interpret deviations from these baselines as indicators of CO2 level changes, improving estimation precision.
Data Source
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AI summary
The present invention relates to a device, system and method for CO2 monitoring. To enable continuous monitoring at low cost and in a simple manner, the device comprises a signal input (10) for obtaining one or more monitoring signals (20) of a monitored area, a breathing monitor (11) for determining one or more breathing parameters (21) of one or more subjects present in the monitored area from the obtained one or more monitoring signals, and a CO2 estimation unit (12) for estimating the CO2 level (22) in the monitored area based on the determined one or more breathing parameters.