Multi-Modal Drowsiness Detection and Awakening Control
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Solution Overview
Problem
Existing wakefulness induction devices struggle to accurately estimate and address the sleepiness of multiple individuals in a shared environment, leading to ineffective wakefulness induction.
Innovation Solution
A wakefulness induction control device that includes a sleepiness estimating system, which detects environmental conditions and calculates a sleepiness level for each individual, activating wakefulness inducers based on individual sleepiness levels and environmental factors to tailor wakefulness induction methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If audio signals are used to detect drowsiness, then the detection method is simple and non-intrusive, but the precision of drowsiness detection is insufficient
Solution Approach 1:
The patent combines multiple detection methods (audio analysis, image recognition, and physiological signal monitoring) into a unified drowsiness detection system. The audio signal detection unit, image recognition unit, and physiological signal detection unit work together to comprehensively assess driver drowsiness, thereby improving detection precision while maintaining ease of operation through automated multi-modal integration.
Solution Approach 2:
The detection system is designed to perform multiple functions simultaneously: it processes audio signals for snoring and coughing detection, analyzes images for eye closure and head position, and monitors physiological signals. This multi-functional approach allows the system to detect various types of drowsiness indicators using a single integrated platform, improving overall detection precision without requiring separate complex systems.
2Measurement precision
If multiple detection methods are combined to improve drowsiness detection precision, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent introduces a central control unit that acts as an intermediary to coordinate the multiple detection methods. This control unit receives data from audio sensors, image cameras, and physiological signal detectors, processes the information centrally, and generates unified drowsiness assessments. The intermediary structure manages the complexity of multiple detection methods while maintaining high detection precision through centralized processing.
Solution Approach 2:
The detection system is divided into distinct functional modules: an audio signal detection unit for snoring and coughing, an image recognition unit for eye and head monitoring, and a physiological signal detection unit. Each module independently processes specific types of data, and their results are integrated by the control unit. This segmentation allows each component to be optimized for its specific function while the overall system maintains high precision through comprehensive multi-modal detection.
3Reliability
If drowsiness detection and awakening induction are integrated, then the system effectiveness is improved, but the control system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the drowsiness detection results directly trigger awakening induction actions. When the detection unit identifies drowsiness conditions (such as eye closure ratio exceeding thresholds or specific head positions), the control unit automatically activates awakening stimuli (visual, auditory, or haptic). This closed-loop feedback system integrates detection and induction functions, improving reliability by ensuring timely awakening responses while managing complexity through automated decision-making algorithms.
Data Source
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AI summary
A sleepiness estimating device (1) includes an environmental information detector (150) that detects a plurality of conditions of an environment surrounding a user (U) and outputs environmental information indicating the detected conditions of the environment, a calculator (2) that calculates an environmental level indicating a degree of how likely the user (U) becomes sleepy in the environment in accordance with the environmental information output by the environmental information detector (150), and an output (130) that outputs the environmental level calculated by the calculator (2).