Fatigue Prediction Using Analogue Brain Wave Data
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Solution Overview
Problem
Existing methods for detecting fatigue state using eye image features or brain wave data are either inaccurate or cumbersome, making them unsuitable for portable detection equipment and resulting in poor user experience.
Innovation Solution
A method and apparatus for fatigue prediction based on analogue brain wave data using a generative adversarial network, which collects eye video sequences, trains a target adversarial network model, and adjusts parameters to obtain a fatigue discriminator for accurate fatigue state discrimination, avoiding tedious operations and improving robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real brain wave detection equipment is used to detect fatigue state, then detection accuracy is improved, but device complexity and detection process become too cumbersome for portable equipment
Solution Approach 1:
The patent uses a generative adversarial network to create a copy of brain wave data characteristics from eye video sequences. Instead of directly measuring brain waves with complex equipment, the system generates synthetic brain wave data that mimics real brain wave patterns, thereby achieving accurate fatigue detection with simpler portable devices.
Solution Approach 2:
The patent introduces eye video sequences as an intermediary between the simple portable camera and the target of fatigue state detection. The eye video data serves as a mediator that can be easily captured by portable devices while still providing information about fatigue state through the trained neural network model.
2Device complexity
If eye image features are used to detect fatigue state, then device complexity is reduced for portable equipment, but detection accuracy becomes insufficient
Solution Approach 1:
The patent transforms the parameter representation by converting eye video sequences into analogue brain wave data through a generative adversarial network. This parameter transformation allows the system to use simple eye video input while producing output that has the characteristic patterns of brain wave data, thereby improving detection accuracy without increasing device complexity.
Solution Approach 2:
The patent replaces the mechanical/physical brain wave detection system with a computational system that processes eye video sequences. Instead of using complex hardware to directly measure brain waves, the system uses neural network processing to substitute the physical measurement process with an information processing approach.
3Ease of operation
If traditional fatigue detection methods are used, then implementation is simpler, but detection accuracy and user experience are poor
Solution Approach 1:
The patent performs preliminary training of the generative adversarial network offline before actual fatigue detection is needed. The model is pre-trained on paired eye video and brain wave data, so that during actual use, the system only needs to perform inference on new eye video sequences, maintaining ease of operation while achieving high accuracy through the pre-learned patterns.
Data Source
AI summary
The present disclosure discloses methods and apparatus for fatigue prediction based on analogue brain wave data, wherein one of the methods comprises: collecting an eye video sequence based on a video capture device; inputting the eye video sequence into a default fatigue discriminator to obtain predicted analogue brain wave data; and outputting the analogue brain wave data to a fatigue discriminant to discriminate a fatigue state. By adopting such a method for fatigue prediction based on analogue brain wave data described in the present disclosure, corresponding analogue brain wave data can be generated through acquiring eye image data, and the fatigue state can be predicted according to the analogue brain waves, so as to avoid tedious operation steps and improve the robustness and accuracy of the fatigue state detection, thereby greatly improving the user experience.