PDF
Full
Outline
In order to enhance accuracy of driving fatigue detection, this paper takes drivers’ physiological signal pulse wave as the data source, introduces hemodynamic-based blood pressure waveform features based on the extraction of Heart Rate Variability (HRV) features. Moreover, a feature indicator set that can effectively characterize driving fatigue is constructed, and a three-classification model of driver fatigue is constructed based on ensemble learning. Then, a resampling method is introduced in the data preprocessing stage, and the effects of different sampling methods on the detection performance of the model are contrasted. Test results show that multidimensional feature fusion of pulse signals can significantly improve the detection accuracy of driver fatigue by 24.68 percentage points on average in all scenarios compared with the method of using only HRV features; resampling can further enhance the detection performance of the ensemble learning model, and the model achieves the best detection performance in a scenario with a sampling window width of 2 min, a sampling window overlap of 80%, and a fusion of HRV features with pulse waveform features.
PDF
229
85
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
