Vehicle Occupant Health Detection Using Sensor and Input Patterns
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Solution Overview
Problem
Current vehicle sensor systems and human-machine interfaces are unable to detect occupant inputs that may indicate health conditions, limiting their ability to monitor and respond to potential health issues during vehicle occupancy.
Innovation Solution
A system comprising multiple vehicle sensors and a controller that performs measurements to determine a health risk probability using machine learning algorithms, incorporating data from control inputs, vehicle movement, mobile device health data, and occupant interactions, with the ability to notify the occupant if the risk exceeds a predetermined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If vehicle sensor systems and human-machine interfaces are used to monitor occupant inputs, then occupant comfort and convenience are improved, but the ability to detect health conditions is lost
Solution Approach 1:
The existing vehicle sensor system is enhanced to perform multiple functions: it continues to provide occupant comfort and convenience through standard control monitoring while simultaneously detecting health conditions by analyzing patterns in occupant inputs. The system processes control measurements for both conventional vehicle operation and health risk assessment, making the sensor system universal in its application.
2Measurement precision
If multiple vehicle sensors and measurements are used to determine health risk probability, then health condition detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the health detection function into distinct components: control measurements from various sensors, vehicle movement measurements for reference, processing measurements to determine occupant control inputs, and a machine learning algorithm to calculate health risk probability. This segmentation allows each component to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The system introduces an intermediary processing layer that transforms raw sensor measurements into meaningful health risk indicators. The controller processes control measurements and vehicle movement measurements to determine occupant control inputs, which then serve as input to the machine learning algorithm. This intermediary step simplifies the relationship between multiple sensors and the final health risk probability output.
3Reliability
If continuous monitoring of occupant control inputs is performed, then early health condition detection is improved, but data processing requirements increase
Solution Approach 1:
The system performs continuous monitoring of occupant control inputs through periodic measurements at defined intervals. The controller regularly obtains control measurements from sensors and processes them to update health risk probability assessments. This periodic action enables early detection of health conditions while managing data processing energy consumption through structured, interval-based processing rather than continuous computation.
Data Source
AI summary
A system for detecting a health condition of an occupant of a vehicle includes a plurality of vehicle sensors and a controller in electrical communication with the plurality of vehicle sensors. The controller is programmed to perform a plurality of measurements using the plurality of vehicle sensors determine a health risk probability based at least in part on the plurality of measurements. The health risk probability is a probability that the occupant of the vehicle has the health condition. The controller is further programmed to notify the occupant of the vehicle in response to determining that the health risk probability is greater than or equal to a predetermined health risk threshold.