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A noninvasive driving fatigue state identification method based on the surface electromyographic signals of the driver's legs is proposed. Firstly, the electromyographic signal of the tibialis anterior muscle of the driver's right leg is collected through a simulated driving fatigue experiment, and the fatigue status is marked through a subjective evaluation scale. Secondly, a variational mode decomposition algorithm is used to filter out noise on the surface electromyographic signal, and 12 timefrequency domain eigenvalues are extracted from the five IMF components obtained by decomposition. Finally, a driving fatigue state discrimination model based on whale algorithm optimized support vector machine is constructed. The results show that this method has a good discrimination effect on three fatigue states, with an accuracy of more than 84%.
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| 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 |
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