Alertness Estimation via Selective Pulse Wave Detection
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Solution Overview
Problem
Existing alertness estimation systems require costly heart rate sensors and increase processing load by continuously detecting heart rate from face images, making them impractical for widespread implementation.
Innovation Solution
An alertness estimation apparatus that extracts eye images from face data, estimates initial alertness based on eyelid states, and detects pulse waves only when the subject is determined to be sleeping, using a neural network to estimate secondary alertness from the detected pulse waves without the need for additional sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heart rate sensors are equipped for each human to detect wakefulness, then measurement precision of alertness is improved, but device complexity and introduction cost increase
Solution Approach 1:
The patent extracts the pulse wave detection function from dedicated heart rate sensors and implements it through image processing of face images. The pulse wave detection unit detects pulse waves by analyzing color changes in facial regions from captured images, eliminating the need for separate sensor devices while maintaining alertness detection capability
Solution Approach 2:
The patent replaces the mechanical/optical sensor-based heart rate detection system with an image processing-based detection system. Instead of using physical sensors to measure physiological signals, the system uses computer vision technology to detect pulse waves through color variations in facial images, substituting a complex hardware system with a software-based solution
2Device complexity
If heart rate is constantly detected from face image to replace sensors, then device complexity is reduced, but processing load increases
Solution Approach 1:
The patent implements periodic action by only performing pulse wave detection when the first alertness estimation indicates the subject may be drowsy. The system periodically estimates alertness based on eye state, and only triggers the computationally intensive pulse wave detection process when necessary, rather than continuously detecting pulse waves
Solution Approach 2:
The patent applies preliminary action by first performing a low-cost alertness estimation based on eye state before initiating pulse wave detection. This preliminary assessment filters out cases where pulse wave detection is unnecessary, preparing the system to only engage in high-processing operations when the initial screening indicates potential drowsiness
3Measurement precision
If pulse wave detection is performed continuously from face image, then measurement precision is improved, but processing load and energy consumption increase
Solution Approach 1:
The system performs pulse wave detection periodically and selectively based on alertness estimation results. When the first alertness estimation unit determines the subject is likely drowsy, the pulse wave detection unit is activated to provide precise measurement. When alertness is normal, pulse wave detection is skipped, maintaining productivity while ensuring measurement precision when needed
Data Source
AI summary
An alertness estimation apparatus 10, includes: an image extraction unit 11 that extracts an image of a portion including eyes from an image data of a face image of a human; a first alertness estimation unit 12 that estimates a first alertness based on the extracted image; a pulse wave detection unit 13 that determines whether or not the estimated first alertness satisfies a set condition, and detect a pulse wave of the human from the face image, when the first alertness satisfies the set condition; and a second alertness estimation unit 14 that estimates a second alertness of the human based on the detected pulse wave.