AI Motion Sickness Prediction for Infants and Sleeping Passengers
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Solution Overview
Problem
Existing technologies for mitigating motion sickness in passengers are ineffective for infants and sleepers who cannot express their discomfort, as they fail to accurately detect and respond to individual variations in motion sickness reactions and states.
Innovation Solution
A system that learns motion sickness prediction models based on vehicle and passenger state information, using AI and machine learning to optimize driving routes, rest timing, and vehicle states, including seat adjustments, to prevent motion sickness in infants and sleepers by predicting reactions and adapting vehicle settings accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional motion sickness detection technology is used, then motion sickness can be detected in awake passengers who can express their discomfort, but infants and sleeping passengers who cannot express their discomfort cannot be accurately detected
Solution Approach 1:
The patent replaces subjective verbal reporting (mechanical/system of passenger expression) with objective biometric sensing (physiological signal detection). Biometric sensors detect physiological signals such as heart rate, skin conductance, and body temperature to objectively determine motion sickness states without requiring passenger expression, thereby enabling detection in infants and sleeping passengers.
Solution Approach 2:
The patent introduces biometric sensors as an intermediary between the passenger's internal physiological state and the vehicle's detection system. These sensors act as mediators that translate invisible physiological changes into measurable data, allowing the system to infer motion sickness states indirectly through physiological indicators rather than direct passenger communication.
2Ease of operation
If generic motion sickness mitigation strategies are applied, then general comfort may be improved, but individual variations in motion sickness reactions and states cannot be addressed
Solution Approach 1:
The patent implements dynamic adjustment of vehicle settings based on real-time biometric data. The system continuously monitors physiological signals and automatically adjusts seat position, air conditioning temperature, and ventilation in response to detected motion sickness states, creating a responsive adaptive system that evolves with passenger needs rather than using static generic settings.
Solution Approach 2:
The system performs preliminary actions by proactively adjusting vehicle settings before severe motion sickness occurs. By continuously monitoring biometric indicators, the system can detect early signs of discomfort and preemptively modify seat position, temperature, or airflow to prevent worsening symptoms, rather than waiting for explicit passenger requests.
3Measurement precision
If multiple biometric sensors are deployed to capture individual variations, then detection accuracy for different passengers improves, but system complexity increases
Solution Approach 1:
The patent employs multi-functional biometric sensors that can detect multiple physiological parameters simultaneously. A single sensor system captures heart rate, skin conductance, and temperature data, eliminating the need for separate dedicated sensors for each parameter. This universal sensing approach maintains high detection accuracy while minimizing the total number of components required.
Solution Approach 2:
The patent combines multiple sensing functions into integrated biometric monitoring units. Rather than deploying separate sensors for each physiological parameter, the system merges detection capabilities into unified sensor assemblies that collect comprehensive biometric data through coordinated operation, reducing overall system complexity while preserving individual detection precision.
Data Source
AI summary
A method of mitigating motion sickness in a passenger including learning a motion sickness prediction model based on state information of a vehicle or the passenger, reaction information of the passenger, and motion sickness-related information, predicting reactions of the passenger to a current state of the vehicle or the passenger and possibility of motion sickness using the learned motion sickness prediction model, and providing information on the motion sickness mitigation methods to the passenger or controlling the vehicle based on the predicted reactions of the passenger and possibility of motion sickness. The motion sickness-related information includes one or more of a determination result of whether motion sickness occurs to the passenger, motion sickness state information, and motion sickness reaction information.