首页|Research on the driver fatigue early warning model of electric vehicles based on the fusion of EMG and ECG signals
Research on the driver fatigue early warning model of electric vehicles based on the fusion of EMG and ECG signals
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维普
Research on the driver fatigue early warning model of electric vehicles based on the fusion of EMG and ECG signals
Electric vehicles have been rapidly developing worldwide due to the use of new energy.Howev-er,at the same time,serious traffic accidents caused by driver fatigue in emergency situations have also drawn widespread attention.The lack of datasets in real vehicle test environments has always been a bottleneck in the research of driver fatigue in electric vehicles.Therefore,this study establi-shes a dataset from real vehicle test,applies the Bayesian optimization support vector machine(BOA-SVM)algorithm to take features of electromyography(EMG)and electrocardiography(ECG)signals as input and develop an early warning model for driving fatigue detection.Firstly,the driver's EMG and ECG signals are collected through real vehicle testing experiments and then combined with the driver's subjective fatigue evaluation scores to establish the dataset.Secondly,the study establishes a driver fatigue early warning model for emergency situations.Time-domain and frequency-domain features are extracted from the EMG signals.Principal component analysis(PCA)is applied for dimensionality reduction of these features.The experimental results show that based on the input of dimensionality reduced EMG features and ECG features,the BOA-SVM algorithm achieved an accuracy of 94.4%in classification.
driver fatigue early warningelectromyography(EMG)signalelectrocardio-graphy(ECG)signalprincipal component analysis(PCA)support vector machine(SVM)
任彬、LI Qibing、ZHOU Qinyu、LUO Wenfa
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Shanghai Key Laboratory of Intelligent Manufacturing and Robotics,School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,P.R.China
Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology,Ningbo Institute of Materials Technology&Engineering,Chinese Academy of Sciences,Ningbo 315201,P.R.China
SAIC Motor R&D Innovation Headquarters,SAIC Motor Corporation Limited,Shanghai 201804,P.R.China
driver fatigue early warning electromyography(EMG)signal electrocardio-graphy(ECG)signal principal component analysis(PCA) support vector machine(SVM)
万方数据
维普
