Drowsiness Detection via Heart Rate Variability Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies lack effective methods to detect drowsiness in users, particularly in scenarios where drowsiness could lead to safety risks, such as driving, as they do not accurately and reliably monitor the user's mental state using heart rate variability and other biometric data.
Innovation Solution
A mobile electronic device equipped with a heart rate sensor and processor that analyzes beat-to-beat intervals and power spectrum to determine drowsiness levels, providing alerts and remedial actions when the user is deemed drowsy, such as vibration and auditory alerts, and offering personalized drowsiness scales based on user-specific data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If heart rate monitoring is used to detect drowsiness, then drowsiness detection capability is improved, but measurement precision and reliability are insufficient
Solution Approach 1:
The patent segments the heart rate signal analysis into multiple components: beat-to-beat interval extraction, power spectrum analysis, and multiple feature parameters (mean, standard deviation, skewness, kurtosis). This segmentation allows each aspect to be analyzed independently and combined for more precise drowsiness detection.
Solution Approach 2:
The patent transforms the raw heart rate signal into multiple derived parameters including beat-to-beat intervals, power spectrum density values, and statistical features (mean, standard deviation, skewness, kurtosis). These parameter transformations enable more accurate detection of drowsiness states from the same physiological signal.
2Device complexity
If simple heart rate monitoring is implemented, then device complexity is reduced, but drowsiness detection reliability deteriorates
Solution Approach 1:
The patent makes the existing heart rate monitor serve multiple functions: not only monitoring heart rate for fitness tracking but also detecting drowsiness through sophisticated signal analysis. This multi-functionality improves reliability without adding separate hardware systems.
Solution Approach 2:
The patent replaces complex mechanical or electronic drowsiness detection systems with advanced software-based analysis of existing heart rate signals. The computational analysis substitutes for additional sensors or complex hardware, maintaining device simplicity while improving reliability.
3Measurement precision
If beat-to-beat interval analysis is performed, then drowsiness detection precision is improved, but processing time and computational load increase
Solution Approach 1:
The patent performs preliminary processing of heart rate signals by continuously extracting beat-to-beat intervals and computing power spectrum density in advance. These pre-computed features are stored and ready for rapid drowsiness assessment when needed, reducing real-time processing requirements.
Solution Approach 2:
The patent computes multiple statistical parameters (mean, standard deviation, skewness, kurtosis) and power spectrum features, using more computational resources than strictly necessary for basic detection. This excessive action ensures high precision by capturing subtle physiological changes that might be missed with minimal processing.
Data Source
AI summary
A mobile electronic device is operable to detect and display a mental state of a user such as drowsiness. The mobile electronic device includes a heartrate sensor, a processor, and a display. The heartrate sensor is operable to provide a heartbeat signal indicative of a heartbeat of the user. The processor is operable to: acquire a beat-to-beat interval based upon the heartbeat signal and determine a drowsiness level of the user based at least in part upon the beat-to-beat interval. The display is operable to display an indication of the drowsiness level.