Driver Biological State Detection Using Aortic Pulse Wave Analysis
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Solution Overview
Problem
Current methods for monitoring a driver's biological state during driving, particularly in preventing dozing and detecting sleepiness, face challenges in accuracy and reliability, often issuing false warnings or missing subjective sleepiness, leading to a need for improved detection methods that can accurately identify hypnagogic symptoms and imminent sleep phenomena.
Innovation Solution
The use of aortic pulse wave (APW) biosignals from the driver's back, analyzed through frequency gradient time series waveforms, absolute value processing, and distribution rate calculations to determine biological states such as hypnagogic symptoms, imminent sleep, and subjective sleepiness, allowing for more accurate and timely warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional frequency analysis methods are used to monitor driver biological state, then the monitoring system can detect fatigue and sleepiness, but the detection accuracy is insufficient leading to false warnings and missed detections
Solution Approach 1:
The patent segments the frequency spectrum into specific bands (ULF: 0.0003-0.003 Hz, VLF: 0.003-0.04 Hz, LF: 0.04-0.15 Hz, HF: 0.15-0.4 Hz) and analyzes power spectra in each band separately. This segmentation allows precise identification of characteristic frequency patterns associated with different biological states, improving detection accuracy while reducing false warnings through multi-band verification
Solution Approach 2:
The patent introduces time-series analysis of power spectra as an additional dimension beyond traditional single-point frequency analysis. By tracking how power spectra evolve over time across multiple frequency bands, the system can distinguish between transient fluctuations and genuine biological state changes, significantly improving warning reliability
2Measurement precision
If the monitoring system uses multiple frequency bands and time series analysis, then detection precision improves, but the device complexity increases
Solution Approach 1:
The patent extracts only the essential features from complex biosignals by focusing on power spectra in predefined frequency bands. Instead of analyzing the entire signal spectrum continuously, the system extracts characteristic power values from specific bands (ULF, VLF, LF, HF) and their temporal variations, simplifying processing while maintaining high detection precision
Solution Approach 2:
The patent transforms the complex biosignal into a simplified parameter set representing power spectra in four frequency bands and their time-series variations. This parameter transformation reduces computational complexity by converting continuous signal analysis into discrete band-power measurements, making the system more efficient while preserving critical biological state information
Data Source
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AI summary
There is provided a technology for grasping a biological state of a driver more accurately. The present invention has a hypnagogic symptom phenomenon detecting means 621, an imminent sleep phenomenon detecting means 622, a subjective sleepiness/low consciousness traveling state detecting means 623, and a homeostasis function level determining means 624, which are configured to function in parallel. Therefore, detection of a hypnagogic symptom phenomenon or an imminent sleep phenomenon, or detection of a period resisting a light sleepiness (mild sleepiness) or a strong sleepiness which occurs consciously, or the case where a low consciousness traveling state due to a decrease in consciousness level momentarily occurs or the case where it occurs longer continuously, or the like, can be determined/detected by the respective means, and the driver's biological state can be determined more accurately than conventional ones.