Statistical Operator Fatigue Assessment Using Physiological Data
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Solution Overview
Problem
Existing methods for assessing operator fatigue are subjective and biased, lacking objective and structural statistics for fatigue analysis, which can lead to inaccurate assessments and potential underestimation of fatigue levels.
Innovation Solution
A method and system for determining statistical fatigue levels in a population of operators by acquiring and preprocessing physiological and contextual data, applying filtering criteria, and correlating data across different collection phases to provide a comprehensive and objective view of fatigue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If subjective fatigue assessment methods (questionnaires, individual declarations) are used, then implementation is simple and quick, but the accuracy and objectivity of fatigue measurement deteriorates due to cultural bias and corporate pressure causing underestimation
Solution Approach 1:
The patent replaces subjective self-reporting mechanisms with objective physiological sensing systems. Multiple sensors detect physiological parameters (heart rate variability, galvanic skin response, temperature, respiration) to automatically assess fatigue levels, eliminating the need for operators to subjectively evaluate and report their own fatigue states.
Solution Approach 2:
The system introduces an intermediary automated assessment mechanism that collects and analyzes physiological data between the operator and the fatigue evaluation process. This intermediary system processes sensor data through algorithms to generate objective fatigue scores, removing the operator from the evaluation loop and preventing bias from cultural or corporate pressures.
2Reliability
If existing individual fatigue assessment methods are used, then specific individual alerts can be generated, but structural statistics and population-level fatigue analysis capabilities are lacking
Solution Approach 1:
The patent merges individual fatigue assessment capabilities with population-level statistical analysis. The system collects physiological data from multiple operators across different locations and time periods, combining individual datasets into aggregated statistical models that reveal population-wide fatigue patterns, trends, and risk factors while maintaining individual assessment functionality.
Solution Approach 2:
The system achieves multi-functionality by simultaneously providing individual fatigue monitoring, population-level statistical analysis, and predictive modeling capabilities. The same physiological sensing and data processing infrastructure serves both individual alert generation and aggregate fatigue trend analysis, eliminating the need for separate systems.
3Measurement precision
If comprehensive physiological data collection is implemented, then objective fatigue measurement is achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the fatigue assessment system into modular components: physiological sensor modules, data transmission modules, processing algorithms, and output interfaces. Each module performs a specific function and can be independently configured or replaced, reducing overall system complexity while maintaining comprehensive data collection capabilities.
Solution Approach 2:
The system implements self-service through automated data collection, processing, and interpretation. The physiological sensors continuously monitor operators and automatically generate fatigue assessments without requiring manual intervention. The system self-calibrates and adapts to individual baseline patterns, reducing the need for complex manual configuration and maintenance.
Data Source
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AI summary
The present invention relates to a method for determining a statistical fatigue level, comprising the following steps: - acquiring (110) a plurality of operator evaluation data determined from physiological data of the operators; - acquiring (120) a plurality of general context data; - acquiring (130) a plurality of particular context data; - preprocessing (150) all of the acquired data; - acquiring (170) a time range for determining the statistical fatigue level and acquiring (170) a filtering criterion, the filtering criterion being based on the general context data and/or the particular context data; - determining (190) from all of the preprocessed data a statistical fatigue level according to the filtering criterion acquired over the acquired time range.