Objective Operator Fatigue Determination Through Estimate Fusion
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Solution Overview
Problem
Existing biomathematical fatigue models fail to accurately and objectively estimate fatigue levels for all types of missions and operators due to reliance on biased personal data and generic principles, neglecting operational factors like weather and transport type.
Innovation Solution
A method combining biomathematical and physiological data using optimal filters and machine learning algorithms to merge fatigue estimates, incorporating recalibration and fusion techniques to determine an objective fatigue level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If biomathematical fatigue models are used to predict fatigue levels, then fatigue prediction capability is provided, but measurement precision deteriorates due to reliance on biased personal data and generic principles
Solution Approach 1:
The patent combines multiple data sources including objective physiological measurements (heart rate, galvanic skin response, respiratory rate), mission data, and environmental factors into a unified fatigue assessment model. This merging of diverse data streams compensates for the limitations of any single source and improves both precision and reliability of fatigue estimation.
Solution Approach 2:
The system continuously monitors physiological parameters and compares them against predicted fatigue levels from the biomathematical model. This feedback loop allows real-time calibration and adjustment of the model, improving measurement precision by correcting deviations between predicted and actual physiological states.
2Adaptability or versatility
If biomathematical models based on average fatigue levels and one-off campaigns are used, then model construction is simplified, but adaptability deteriorates for different mission types and operators
Solution Approach 1:
The patent creates a universal fatigue assessment system that can be applied across different mission types (aerial, ground, maritime) and operator roles. The model integrates multiple functional components (physiological monitoring, environmental sensing, mission parameter tracking) that work together to provide adaptability without requiring separate models for each application.
Solution Approach 2:
The system segments the fatigue assessment into independent modular components: physiological parameter collection, environmental factor monitoring, mission data processing, and integrated model prediction. This segmentation allows each component to be optimized independently while maintaining overall system versatility across different mission types.
3Reliability
If generic biomathematical principles are used, then model development is easier, but reliability deteriorates by not reflecting operational reality
Solution Approach 1:
The patent incorporates specific operational parameters such as weather conditions, season, transport type, and mission duration into the fatigue model. These parameter changes allow the model to reflect actual operational reality by adjusting fatigue predictions based on environmental and contextual factors that vary across different mission scenarios.
Data Source
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AI summary
The present invention relates to a method for determining (200) an objective fatigue level of an operator carrying out a mission, the method comprising the following steps: - acquisition (210) of a first fatigue estimate determined by a biomathematical fatigue prediction model from a plurality of mission data relating to the mission; - acquisition (220) of a second fatigue estimate determined by a physiological fatigue prediction model from physiological data of the operator; and - determination (230) of an objective fatigue level by merging the two fatigue estimates.