Reduced Biometric Sleep Prediction for Real-Time Drowsiness Alerts
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Solution Overview
Problem
Existing methods for detecting and predicting transitions between awake, drowsiness, and sleep phases are inaccurate, especially when the full photoplethysmography (PPG) signal is not accessible or of poor quality, and they fail to provide real-time alerts for cognitive performance decline, which can lead to safety risks.
Innovation Solution
A statistical analysis approach using a reduced set of biometric data, including heart rate variability (HRV) and respiration rate (RR), processed by edge devices or contactless sensors, to classify drowsiness levels into a four-level scale, enabling real-time detection and prediction of sleep onset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full PPG signal is used for sleep detection, then measurement precision is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the essential biometric parameters (heart rate, respiration rate, heart rate variability) from the complete PPG signal. Instead of processing the entire signal spectrum, the system selectively extracts specific features that are most predictive of sleep onset, thereby reducing computational complexity while preserving detection accuracy.
Solution Approach 2:
The patent creates simplified copies of the PPG signal by generating derived parameters (HRV, RR) that capture the essential sleep-related information. These derived parameters serve as compressed representations that maintain predictive power for sleep detection while requiring significantly less processing resources than the full signal.
2Measurement precision
If comprehensive biometric analysis is performed, then sleep detection accuracy is improved, but real-time processing capability deteriorates
Solution Approach 1:
The patent segments the complex biometric analysis into distinct, computationally efficient components: heart rate calculation, respiration rate extraction, and heart rate variability analysis. Each segment is optimized for rapid computation, allowing the system to process multiple parameters in real-time without sacrificing the comprehensiveness of the sleep detection methodology.
3Ease of operation
If subjective sleepiness scales (e.g., KSS) are used, then ease of operation is improved, but reliability deteriorates due to subjectivity and environmental influence
Solution Approach 1:
The patent introduces biometric parameters as intermediary measurements that objectively reflect the physiological state underlying subjective sleepiness. Instead of directly measuring subjective feeling, the system uses HRV, RR, and heart rate as mediating indicators that correlate with sleep onset, providing an objective proxy that eliminates the subjectivity and environmental sensitivity of self-reported scales.
4Ease of operation
If image-based measures are used for drowsiness detection, then non-intrusiveness is improved, but reliability deteriorates in cases of obstructive sleep apnea or when facial tracking is difficult
Solution Approach 1:
The patent develops a multi-functional detection system that can operate through multiple physiological pathways. By monitoring heart rate, respiration rate, and heart rate variability simultaneously, the system provides reliable sleep detection regardless of whether the subject has obstructive sleep apnea or presents challenges for facial tracking. The same biometric framework adapts to different physiological conditions and monitoring scenarios.
Data Source
AI summary
A software storable in, and executable by, electronic processing resources and designed to cause, when executed, the electronic processing resources to become configured to real-time detect and/or predict one or more behavioural states and/or transitions among Awake, Drowsiness and Sleep phases of a subject. The software is designed to cause, when executed, the electronic processing resources to become configured to: receive a biometric signal of a subject; process the received biometric signal to classify it into one of different classes associated with the one or more behavioural states and/or transitions among Awake, Drowsiness and Sleep phases of a subject; and detect and/or predict a behavioural state and/or a transition among awake, Drowsiness, and Sleep phases of the subject based on the classified biometric signal. The software is designed to cause, when executed, the electronic processing resources to become configured to: compute at least a first one synthetic quantity for and based on the received biometric signal; compute at least one threshold for the received biometric signal based on the at least first one synthetic quantity computed therefor; and classify the biometric signal into one of different classes associated with the one or more behavioural states and/or transitions among awake, Drowsiness, and Sleep phases based on threshold computed therefor.