Driver State Estimation via Four-Quadrant Coordinate Plotting
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Solution Overview
Problem
Current systems lack the accuracy in detecting a driver's state, particularly distinguishing between normal fatigue, alcohol intake, and slump states, especially after driving duties, which can lead to severe accidents if not monitored effectively.
Innovation Solution
A biological state estimation device that analyzes fluctuations in ultra-low-frequency bands of biological signals, using frequency analysis and trigonometric representations to differentiate between harmonic oscillation and irregular vibration systems, and plots these changes on a four-quadrant coordinate system to estimate the driver's state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pulse wave analysis methods are used, then the device complexity is reduced, but the measurement precision for detecting driver states (fatigue, alcohol, slump) deteriorates
Solution Approach 1:
The frequency spectrum is segmented into multiple bands (0.04-0.15Hz, 0.15-0.4Hz, 0.4-2Hz) and each band is analyzed separately to extract specific physiological information. This segmentation allows precise detection of different driver states by focusing on characteristic frequency ranges for each state type.
Solution Approach 2:
The analysis transitions from single-dimensional pulse wave amplitude measurement to multi-dimensional frequency spectral analysis. By transforming the pulse wave into its frequency components across multiple bands, the system gains additional dimensions of information that enable accurate differentiation between fatigue, alcohol, and slump states.
2Measurement precision
If comprehensive frequency analysis across multiple bands is performed, then the measurement precision for distinguishing driver states improves, but the calculation time and processing complexity increase
Solution Approach 1:
Frequency filtering and spectral analysis are performed in advance during the driving period to establish baseline characteristics. This preliminary analysis allows real-time state detection to be performed more efficiently by comparing current measurements against pre-established frequency patterns for different states.
Solution Approach 2:
Each frequency band is analyzed with specific local characteristics in mind - the 0.04-0.15Hz band for fatigue detection, 0.15-0.4Hz for alcohol detection, and 0.4-2Hz for slump detection. This localized analysis approach optimizes processing efficiency by focusing computational resources on specific frequency ranges relevant to each state type rather than uniformly analyzing all frequencies.
Data Source
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AI summary
Provided is a technique for more accurately ascertaining a person's status. In particular, the present invention makes it possible to accurately ascertain the detection of alcohol or the like. A fluctuation waveform of an ultra-low-frequency band is found from a biosignal collected from a biosignal measuring means, the fluctuation waveform is plotted as coordinate points on a four-quadrant coordinate system on the basis of a predetermined standard, and biological status is estimated on the basis of temporal change in the coordinate points. According to the method of the present invention for plotting a fluctuation waveform as coordinate points on a four-quadrant coordinate system on the basis of the predetermined standard, change in the fluctuation waveform of the ultra-low-frequency band can be expanded or highlighted and thus perceived, and therefore the present invention is adapted for more accurately perceiving change in a person's status.