Self-Learning Motion Sickness Profile System
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Solution Overview
Problem
Current methods for mitigating motion sickness in vehicles are not individualized and fail to adapt effectively to personal differences in vulnerability and countermeasure effectiveness, leading to inadequate relief for vehicle occupants.
Innovation Solution
A self-learning system utilizing artificial intelligence to create and optimize individual profiles for motion sickness susceptibility, incorporating data from various sources such as questionnaires, physiological monitoring, and vehicle settings, which predicts and adjusts countermeasures in real-time to prevent or reduce motion sickness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a standardized questionnaire is used to determine motion sickness tendency, then the determination process is simple and quick, but the result lacks individualization and cannot adapt to personal differences in vulnerability
Solution Approach 1:
The system dynamically adapts the motion sickness determination from a static questionnaire-based approach to a continuous, multi-source data collection process that updates individual profiles over time, enabling the system to evolve from generic to highly individualized assessments
Solution Approach 2:
The system implements feedback loops where questionnaire results, physiological data, and actual motion sickness occurrences are continuously fed back to refine and update individual motion sickness profiles, improving the accuracy and personalization of future predictions
2Ease of operation
If countermeasures are selected based on general guidelines, then the selection process is straightforward, but the effectiveness varies significantly across different individuals
Solution Approach 1:
The system applies local quality by tailoring countermeasure recommendations to each individual's specific motion sickness profile, physiological characteristics, and historical response data, rather than applying uniform general guidelines to all users
Solution Approach 2:
The system changes parameters by adjusting countermeasure selection based on multiple varying factors including individual susceptibility profiles, current physiological state, environmental conditions, and historical effectiveness data, optimizing recommendations for each specific situation
3Measurement precision
If multiple data sources are integrated for individual profiling, then the accuracy of motion sickness prediction improves, but the system complexity increases
Solution Approach 1:
The system merges multiple data sources including questionnaire results, physiological sensor data, vehicle motion data, and environmental information into a unified individual motion sickness profile, integrating diverse inputs to achieve comprehensive and accurate predictions
Solution Approach 2:
The system implements a multi-functional platform that handles data collection, processing, storage, analysis, and recommendation generation within a single integrated architecture, reducing overall system complexity despite the multiple functions performed
4Adaptability or versatility
If the system continuously learns and updates individual profiles, then the personalization and effectiveness of recommendations improve, but the computational requirements and processing time increase
Solution Approach 1:
The system employs periodic action by updating individual motion sickness profiles at strategically determined intervals based on data availability, journey stages, and changes in physiological or environmental conditions, rather than continuously processing all data in real-time
Solution Approach 2:
The system performs preliminary action by pre-processing and storing raw data from various sources during data collection phases, and pre-establishing baseline profiles before journeys begin, reducing the computational burden during actual motion sickness prediction and countermeasure recommendation phases
Data Source
AI summary
Systems and methods for avoiding or mitigating motion sickness in a vehicle are disclosed herein. The systems and methods may include (a) determining a profile of the individual inclination towards motion sickness; (b) predicting an individual effectiveness of countermeasures based on typing using the data from step (a); (c) evaluating the actual effectiveness of the predicted countermeasures after their implementation when travelling in the vehicle; wherein step (a) is repeated regularly and all data and results from step (c) are fed into a self-learning system in order to obtain an improved statement on the individual actual effectiveness of countermeasures taken and to make the selection of effective countermeasures based on them.