Vehicle Occupant Health Monitor Using Sensor Segmentation
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Solution Overview
Problem
Existing vehicle monitoring systems fail to accurately detect and respond to unforeseen medical conditions of drivers, such as heart attacks or seizures, which can lead to accidents due to the lack of verification of diagnostic accuracy and appropriate response.
Innovation Solution
A vehicle occupant health monitoring system that includes a custom health profile module, sensors to monitor physiological conditions, and a control module to perform preestablished actions when a triggering physiological condition and event are detected, allowing for real-time mitigation of potential health situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driver drowsiness monitoring systems are implemented to detect unsafe medical conditions, then driver safety is improved, but the accuracy of diagnostic detection deteriorates due to lack of verification
Solution Approach 1:
The monitoring system is divided into multiple independent sensor modules (heart rate sensor, respiration sensor, muscle tension sensor, temperature sensor) that each monitor specific physiological parameters. This segmentation allows for more precise and verified diagnostic detection of medical conditions while maintaining driver safety.
Solution Approach 2:
The system continuously monitors physiological parameters and provides feedback to the control unit, which compares actual readings against baseline profiles. When deviations indicate a medical condition, the system provides feedback alerts to the driver and can trigger automated responses, thereby verifying diagnostic accuracy while maintaining safety.
2Measurement precision
If multiple sensors and verification mechanisms are added to improve diagnostic accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The control unit serves multiple functions: it stores baseline health profiles, processes data from multiple sensor types, performs diagnostic analysis, provides user feedback, and controls automated vehicle responses. This multi-functionality consolidates what would otherwise be separate complex systems into a single integrated unit.
Solution Approach 2:
The system automatically establishes baseline profiles during initial operation without requiring manual input. It self-calibrates by learning the driver's normal physiological patterns over time, reducing the need for complex setup procedures and manual configuration.
3Reliability
If real-time physiological monitoring is implemented, then driver safety is improved, but energy consumption increases
Solution Approach 1:
The system employs periodic sampling of physiological parameters rather than continuous monitoring at full resolution. Sensors take measurements at optimized intervals based on the driver's state and risk levels, reducing overall energy consumption while maintaining adequate safety monitoring.
Solution Approach 2:
The monitoring intensity dynamically adjusts based on the driver's physiological state. When parameters are within normal ranges, monitoring operates at lower intensity. When deviations are detected, the system increases sampling frequency and activates additional sensors, optimizing energy use according to actual risk levels.
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 reduces the risk of accidents by accurately detecting and responding to health crises through customizable health profiles and sensor monitoring, ensuring appropriate actions are taken when a driver's condition deteriorates, thereby enhancing safety.
Implementation Method 1
The sensor is a micro gesture detecting camera configured to detect one of a vehicle occupant's pulse, blood pressure, stress level, body temperature, or respiration
Data Source
AI summary
Apparatus, systems, and methods for methods for monitoring the health status of a vehicle occupant by detecting when a triggering physiological condition is met, confirming the triggering physiological condition is real by identifying a separate vehicle triggering event, and automatically responding to minimize the potential impact of the triggering event.