Fatigue Analysis Component Combining Visual Behavior and Sleep History
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Solution Overview
Problem
Current methods for determining user fatigue levels are inaccurate and do not effectively combine real-time visual behavior and sleep quality history to provide a comprehensive assessment, especially in critical activities like driving where fatigue can pose risks.
Innovation Solution
A user fatigue level analysis component that combines real-time visual behavior parameters, such as eyelid activity and head movement, with sleep quality history parameters, including environmental and physiological data, using sensors and a processor to determine fatigue levels with enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only real-time visual behavior parameters are used to determine fatigue level, then the measurement can be obtained quickly, but the accuracy of fatigue level determination is insufficient
Solution Approach 1:
The patent combines real-time visual behavior parameters (eyelid activity, head movement) with historical sleep quality parameters into a unified fatigue assessment system. This merging of multiple data sources improves measurement precision by providing a more comprehensive view of user fatigue state, while the modular architecture manages system complexity through organized data integration.
Solution Approach 2:
The system performs preliminary measurement and storage of sleep quality parameters during sleep periods before the actual fatigue assessment is needed. This preliminary action allows the system to have historical baseline data ready when real-time visual behavior monitoring occurs, improving accuracy without adding complexity to the real-time assessment process.
2Measurement precision
If multiple parameters are combined for fatigue assessment, then the accuracy improves, but the device complexity increases
Solution Approach 1:
The patent segments the fatigue assessment system into distinct functional modules: visual behavior monitoring module, sleep quality monitoring module, data integration module, and fatigue level determination module. Each module handles specific parameters independently, improving measurement precision through comprehensive data collection while managing complexity through modular organization and clear separation of concerns.
3Measurement precision
If real-time visual behavior monitoring is implemented, then fatigue detection speed improves, but measurement precision is insufficient without sleep history
Solution Approach 1:
The system performs preliminary measurement and storage of sleep quality parameters during sleep periods before the actual fatigue assessment is needed. This preliminary action allows the system to have historical baseline data ready when real-time visual behavior monitoring occurs, improving accuracy without adding complexity to the real-time assessment process.
Data Source
AI summary
The disclosed embodiments include user fatigue level analysis components, real-time visual behavior measuring devices, and methods for determining the fatigue level of a user. In one embodiment, a user fatigue level analysis component includes a memory configured to store computer executable instructions and a processor for executing the computer executable instructions. The computer executable instructions comprise instructions for receiving at least a visual behavior parameter indicative of the real time visual behavior of the user, for providing at least one sleep quality history parameter indicative of the sleep quality history of the user, and for determining the fatigue level of the user based on the analysis of the combination of the at least one visual behavior parameter and the at least one sleep quality history parameter.