Driver Vigilance Prediction via Non-Contact Physiological Monitoring
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Solution Overview
Problem
Current systems for predicting a driver's level of alertness are insufficient in detecting real-time changes and are not ergonomic or safe for use during driving, as they often require electrodes for EEG analysis or cause driver distraction with camera-based systems that need adaptation and interaction.
Innovation Solution
A method and device that use a high-definition camera and infrared LEDs to acquire and process physiological data, such as eye blinks and head movements, combined with electroencephalographic data profiles from a calibration phase to provide a statistical prediction of alertness on a continuous scale without requiring driver interaction or adaptation, using a database to compare and determine resemblance criteria for real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EEG electrodes are used to detect driver alertness with high precision, then measurement precision is improved, but ease of operation deteriorates due to ergonomic issues and safety concerns during driving
Solution Approach 1:
The patent extracts the detection function from invasive EEG electrodes and implements it through non-contact optical sensors (cameras) that capture physiological signals such as eye movements, blinking patterns, and facial expressions. This extraction eliminates the need for physical electrode contact while preserving the core function of alertness detection.
Solution Approach 2:
The patent replaces the mechanical/electrical EEG electrode system with an optical detection system using cameras and image processing algorithms. This substitution transitions from direct physical measurement (electrodes on scalp) to indirect optical measurement (facial and ocular movements), thereby improving ease of operation while maintaining detection capability.
2Adaptability or versatility
If camera-based alertness systems require driver interaction for adaptation, then adaptability is improved, but ease of operation deteriorates due to driver distraction
Solution Approach 1:
The patent performs all necessary system adaptation and driver-specific parameter calibration during the preliminary recording phase before driving begins. The system captures baseline physiological data and establishes individual patterns in advance, eliminating the need for real-time interaction or adjustment during the actual driving task, thus preventing driver distraction.
Solution Approach 2:
The system performs self-adaptation by automatically analyzing recorded physiological data and adjusting detection parameters without requiring driver input or confirmation. The driver simply needs to be recorded during a brief pre-drive period, after which the system autonomously adapts to their specific patterns and begins monitoring without further interaction.
3Ease of operation
If simple camera systems are used for alertness detection, then ease of operation is improved, but measurement precision deteriorates due to inability to detect real-time changes
Solution Approach 1:
The patent implements dynamic, continuous monitoring using high-frame-rate camera recording and real-time image processing. The system continuously analyzes physiological parameters (blinking frequency, eye closure duration, facial muscle movements) and dynamically updates alertness assessments, enabling precise real-time detection while maintaining non-invasive operation through standard camera technology.
Data Source
Figure 1
AI summary
The invention relates to a device for predicting (10) a vigilance level of a driver, comprising a camera (11) for acquiring images of said driver, an image processing module (13.1) for characterising, from the acquired images, physiological data of said driver, means for storing profiles of the change in electro-encephalographic and physiological data of a set of standard drivers, acquired in a synchronised manner in the course of driving tests during a calibration phase, and a detection module (13.2) capable of comparing the change in physiological data of said driver with the stored profiles of the change in physiological data in order to determine at least one profile satisfying resemblance criteria and performing a statistical prediction of the change in the vigilance level of said driver from the analysis of the stored profile of electro-encephalographic data corresponding to the profile of changes in physiological data having satisfied the resemblance criteria.