| REN Bin (任 彬)* **,LI Qibing*,ZHOU Qinyu*,LUO Wenfa***.[J].高技术通讯(英文),2024,30(4):333~343 |
|
| Research on the driver fatigue early warning model of electric vehicles based on the fusion of EMG and ECG signals |
| |
| DOI：10. 3772 / j. issn. 1006-6748. 2024. 04. 001 |
| 中文关键词: |
| 英文关键词: driver fatigue early warning, electromyography ( EMG) signal, electrocardiography (ECG) signal, principal component analysis (PCA), support vector machine (SVM) |
| 基金项目: |
| Author Name | Affiliation | | REN Bin (任 彬)* ** | (* 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) | | LI Qibing* | | | ZHOU Qinyu* | | | LUO Wenfa*** | |
|
| Hits: 52 |
| Download times: 93 |
| 中文摘要: |
| |
| 英文摘要: |
| Electric vehicles have been rapidly developing worldwide due to the use of new energy. However, 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 establishes 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. |
|
View Full Text
View/Add Comment Download reader |
| Close |
|
|
|
