Multimodal Sensor System for Early Disease Outbreak Detection
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Solution Overview
Problem
Current methods lack effective real-time detection and prevention systems for airborne diseases spreading among mammal populations, particularly in environments where humans and animals converge, such as at watering points, leading to increased risk of disease transmission.
Innovation Solution
A system and method utilizing multiple sensors (audio, motion, and image) to continuously monitor mammal environments, compare data with known disease symptoms, and generate alerts to prevent or control disease outbreaks, integrated with a processor and memory for real-time analysis and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors in different data formats are used to monitor mammal environments, then measurement precision and detection capability are improved, but device complexity increases
Solution Approach 1:
The system divides the monitoring task into multiple independent sensor modules (audio sensors for cough detection, motion sensors for activity monitoring, image sensors for visual assessment), each optimized for specific disease symptom detection. This segmentation allows high measurement precision for each symptom type while managing overall system complexity through modular architecture.
Solution Approach 2:
The processor is designed to handle multiple data formats from different sensor types through a unified analysis framework. The system can detect various disease symptoms (coughing, lethargy, isolation behavior) using the same core processing architecture, making the system universal and reducing complexity compared to having separate specialized systems for each symptom.
2Reliability
If continuous monitoring of mammal populations is implemented, then reliability of disease detection is improved, but loss of time and energy consumption increase
Solution Approach 1:
The system implements continuous monitoring through periodic sampling of sensor data at optimized intervals. Instead of processing every single data point in real-time, the system periodically analyzes aggregated data patterns, maintaining high detection reliability while reducing computational time and energy consumption.
Solution Approach 2:
The system pre-processes and stores baseline health data from sensors during normal periods. When analyzing current data, it compares against pre-established patterns of disease symptoms, enabling rapid detection without extensive real-time computation. This preliminary action reduces the time needed for actual disease outbreak detection.
3Measurement precision
If multimodal sensor data is analyzed to detect disease symptoms, then detection accuracy is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The processor acts as an intermediary that standardizes and integrates data from multiple sensor formats. It converts audio, motion, and image data into a unified representation that can be analyzed together, reducing the difficulty of detecting and measuring disease symptoms across different modalities while maintaining high detection accuracy.
Solution Approach 2:
The system transforms raw sensor data into standardized parameters that represent disease symptoms (e.g., cough frequency from audio data, activity level from motion data). By changing the parameter representation to a common format, the system enables accurate multimodal analysis while simplifying the integration process.
Data Source
AI summary
Methods are provided for detection of a disease breakout from a mammal population. For example, the method involves continuously receiving, from two or more sensors positioned in a mammal environment, information of a population of mammals, wherein the information received from each of the two or more sensors is in a different data format; comparing the received information of the population of mammals or other animals with known disease symptom information; and in response to determining that the information of the population of mammals exceeds a pre-determined threshold alert level, generating an alert to prevent or control the disease; wherein the steps of the method are performed in accordance with a processor and a memory.