Research On Multi-Mode Neural Network Method For Pilot Fatigue Detection

Fatigue detection is a very important research field because fatigue status seriously affects individual productivity and work safety.Failure to identify and handle fatigue conditions in a timely manner may lead to adverse consequences.Therefore,effective detection and management of fatigue status is very important to ensure people’s physical safety and contribute to promoting social progress.Due to the fact that fatigue state is a highly individualized and strongly subjective physiological state,it becomes more difficult to model and analyze fatigue state.The focus of this study is to use multimodal physiological signals(EEG,ECG,and EMG)to detect operator fatigue in a simulated flight operating environment.In this way,more comprehensive and accurate fatigue state information can be obtained.The relevant research in this article is as follows:(1)Establish a simulated flight experiment platform and conduct relevant simulated flight experiments,and synchronously collect various bioelectric signal data such as EEG,EMG,and ECG to ensure the synchronization and accuracy of data in the experiment.(2)To preprocess collected physiological data,we need to perform preprocessing operations based on the characteristics of different signal types,including steps such as noise reduction and filtering,to eliminate noise and interference.This is a necessary step.Then use the Relief algorithm to select the fatigue characteristics of EEG,ECG,and EMG.(3)Multimodal physiological data fusion fatigue decision-making model.In this thesis,the selected fatigue features are spliced into a single vector through features,and then fatigue classification is performed through machine learning algorithms.The research results show that traditional machine learning algorithms have performance bottlenecks in current tasks,mainly due to their dependence on artificial feature extraction operations.In contrast,after simple filtering and preprocessing of time-domain data,using a Transformer neural network model with an attention mechanism can directly extract important features from bioelectrical signals,and can better capture key information when processing signals.This performance improvement can be demonstrated through higher accuracy and clearer confusion matrices,making its performance superior to traditional machine learning algorithms.To sum up,this study aims to explore how to effectively detect operator fatigue in simulated flight environments and propose a solution based on deep learning.The scheme uses a Transformer deep neural network equipped with an attention mechanism,and has achieved excellent performance in experiments.The experimental results show that the model performs well on several key indicators.In addition,the model has good adaptability and can be applied to operator fatigue detection in different production environments.In contrast,there are certain bottlenecks in the performance of traditional machine learning methods.The findings of this study not only provide important reference and guidance for the further improvement of fatigue detection and management methods,but also have high practical application value.