Travel Environment Control System for Passenger Wellness
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Solution Overview
Problem
Current systems for controlling the travel environment, such as in aircraft cabins, lack efficiency and personalization, particularly in alleviating travel fatigue like jet lag, and do not effectively utilize passenger data and environmental sensors to dynamically adjust conditions for enhanced passenger wellness.
Innovation Solution
A system that automatically controls the travel environment by obtaining passenger data and sensor inputs to generate a dynamic event schedule, adjusting seat and environmental settings such as lighting and air conditioning, based on the passenger's itinerary, preferences, and physiological state, to optimize comfort and reduce jet lag.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data entry is required from passengers, then data accuracy may be improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically retrieves and processes passenger data from existing sources without requiring manual entry by passengers. The system serves itself by autonomously collecting itinerary information, preferences, and physiological data from multiple sources, then processing this data to generate personalized environment control schedules.
Solution Approach 2:
The system acts as an intermediary between existing data sources (airline reservation systems, wearable devices, sensors) and the environment control system. It automatically collects, integrates, and processes data from these sources, eliminating the need for direct manual input from passengers while ensuring data accuracy through systematic retrieval and validation.
2Adaptability or versatility
If static environment control is used, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system dynamically adjusts the passenger environment based on real-time physiological data, itinerary information, and environmental conditions. It generates and updates personalized schedules that adapt to changing passenger needs throughout the journey, transforming static environment control into a dynamic, responsive system.
Solution Approach 2:
The system segments the travel journey into distinct phases (pre-flight, in-flight, post-flight) and creates personalized environment control schedules for each segment. It divides the control of various environmental parameters (lighting, temperature, seat position) into independently adjustable components, allowing flexible adaptation without overwhelming system complexity.
3Measurement precision
If real-time sensor monitoring is implemented, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system employs periodic sampling of physiological data from wearable devices and sensors rather than continuous monitoring. It collects data at strategically timed intervals relevant to different flight phases, maintaining measurement precision for critical parameters while significantly reducing overall energy consumption compared to continuous monitoring.
Solution Approach 2:
The system performs preliminary processing and filtering of sensor data to identify only the most relevant measurements that require full attention. It pre-identifies critical physiological parameters and environmental conditions that warrant detailed monitoring, reducing energy expenditure on processing less significant data while maintaining high measurement precision for key metrics.
Data Source
AI summary
A system and method for controlling the travel environment for a passenger are described, in which passenger data is obtained from an existing source of stored data, the stored data including information on the passenger's itinerary. One or more sensor inputs are received, providing information on the physiological state of the passenger and/or environmental conditions in the vicinity of the passenger. One or more outputs are provided to control the passenger's travel environment based on the passenger data and the one or more sensor inputs. A system and method of dynamic travel event scheduling is also described, in which a dynamic event schedule is generate based on the retrieved data, the dynamic event schedule including at least one event associated with at least one action output.