Camera-Based Fatigue Detection via Optical Feature Extraction
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Solution Overview
Problem
Current methods for detecting worker fatigue often rely on contact-based physiological signal collection, which is slow and limits quick analysis, affecting application efficiency in various industries.
Innovation Solution
A fatigue data generation system and method that combines contact and contactless detection using a camera device and processor to analyze images and generate fatigue data through a fatigue analysis model incorporating reference physiological signals, feature data, and correlation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contact-based physiological signal detection is used, then measurement precision can be improved, but detection speed and analysis time deteriorate
Solution Approach 1:
The patent replaces contact-based mechanical/physical physiological signal detection with contactless optical detection using a camera. The camera captures images of physiological indicators (such as facial color, skin texture, or specific body regions) and uses image processing algorithms to extract physiological information without physical contact, thereby eliminating the time delay associated with contact-based measurement and analysis while maintaining detection accuracy.
Solution Approach 2:
The patent creates an optical copy (image) of the physiological state through camera capture. Instead of directly measuring physiological signals through contact sensors, the system captures visual representations of physiological indicators and processes these image copies to infer the actual physiological state, enabling rapid analysis without the time constraints of direct contact measurement.
2Measurement precision
If contact-based detection methods are used, then physiological signal accuracy is improved, but application efficiency deteriorates
Solution Approach 1:
The patent substitutes contact-based physiological detection with contactless optical detection using camera imaging. This replacement eliminates the need for physical sensor attachment and complex signal processing pipelines, enabling rapid deployment across multiple subjects simultaneously and significantly improving application efficiency while maintaining physiological measurement accuracy through advanced image analysis.
Solution Approach 2:
The camera-based system serves multiple functions: it captures physiological indicators, extracts feature data through image processing, and generates fatigue assessments. This multi-functional approach replaces specialized contact sensors with a single versatile device, improving application efficiency by enabling simultaneous monitoring of multiple physiological parameters without requiring separate contact-based measurement systems for each parameter.
3Speed
If rapid fatigue estimation is implemented, then analysis speed is improved, but measurement precision may deteriorate
Solution Approach 1:
The patent performs preliminary actions by capturing images that contain multiple physiological indicators simultaneously. The image processing system pre-extracts relevant features (such as color information, texture patterns, or facial expressions) from the captured images, preparing the data for rapid fatigue assessment. This preliminary feature extraction enables quick analysis while maintaining precision by ensuring that all necessary physiological information is captured and processed in advance.
Solution Approach 2:
The patent transforms physiological information from one parameter domain to another by converting visual image parameters (color, brightness, texture) into physiological state parameters (fatigue level). This parameter transformation enables rapid estimation by using easily measurable optical parameters that correlate with physiological states, achieving both speed and accuracy through appropriate parameter selection and correlation.
Data Source
AI summary
A fatigue data generation method, comprising: obtaining, by the camera device, a target image; obtaining, by a processor, a target feature data from the target image, and inputting the target feature data to a fatigue analysis model which stored in a storage unit, wherein the fatigue analysis model comprises a plurality of reference physiological signals, a plurality of reference feature data, a plurality of reference fatigue data and a plurality of correlation parameters; and generating a target fatigue data according to the target feature data, the plurality of reference feature data and the plurality of correlation parameters.