76
A. Boudaya et al.
Table 3. Accuracy, precision, recall and F1-score of our experimental configuration
Accuracy Precision Recall F1 score
93.94%
87.29%
99.79% 93.12%
Table 4. Accuracy comparison with related works.
Drowsiness detection methodology Accuracy Classification method
R. Osmalina et al. [24]
91.67%
CSP algorithm
Proposed method
93.94 % CNNs
4 Conclusion
The present work proposes a CNN based approach for Hypo-vigilance detection.
In order to create a EEG dataset, we recorded raw EEG data using Epoc+
headset. The suggested system achieves an average classification accuracy to
93.94% by testing it on a real dataset of eight participants. In future work, we
will focus to improve classification accuracy with large datasets. Additionally,
fusion with other biomedical signals should be also considered to improve the
classification accuracy.
References
1. Hu, J., Wang, P.: Noise robustness analysis of performance for EEG-based driver
fatigue detection using different entropy feature sets. Entropy 19, 385 (2017)
2. Thomas, L.C., Gast, C., Grube, R., Craig, K.: Fatigue detection in commercial
flight operations: results using physiological measures. Procedia Manuf. 3, 2357–
2364 (2015)
3. Neri, D.F., Shappell, S.A., DeJohn, C.A.: Simulated sustained flight operations
and performance, part 1: effects of fatigue. Mil. Psychol. 4, 137–155 (1992)
4. Chaari, L., Golubnitschaja, O.: Covid-19 pandemic by the “real-time” monitoring:
the Tunisian case and lessons for global epidemics in the context of 3PM strategies.
EPMA J. (2020)
5. Sahayadhas, A., Sundaraj, K., Murugappan, M.: Electromyogram signal based
hypovigilance detection. Biomed. Res. (India) 25, 281–288 (2014)
6. Wang, F., Wang, H., Fu, R.: Real-time ECG-based detection of fatigue driving
using sample entropy. Entropy 20(3), 196 (2018)
7. Ahn, S., Nguyen, T., Jang, H., Kim, J.G., Jun, S.C.: Exploring neuro-physiological
correlates of drivers’ mental fatigue caused by sleep deprivation using simultaneous
EEG, ECG, and fNIRS data. Front. Hum. Neurosci. 10, 219 (2016)
8. Basri, C., et al.: Muscle fatigue detections during arm movement using EMG signal.
IOP Conf. Ser. Mater. Sci. Eng. 557, 012004 (2019)
9. Alom, M.Z., et al.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3), 292 (2019)
A. Boudaya et al.
Table 3. Accuracy, precision, recall and F1-score of our experimental configuration
Accuracy Precision Recall F1 score
93.94%
87.29%
99.79% 93.12%
Table 4. Accuracy comparison with related works.
Drowsiness detection methodology Accuracy Classification method
R. Osmalina et al. [24]
91.67%
CSP algorithm
Proposed method
93.94 % CNNs
4 Conclusion
The present work proposes a CNN based approach for Hypo-vigilance detection.
In order to create a EEG dataset, we recorded raw EEG data using Epoc+
headset. The suggested system achieves an average classification accuracy to
93.94% by testing it on a real dataset of eight participants. In future work, we
will focus to improve classification accuracy with large datasets. Additionally,
fusion with other biomedical signals should be also considered to improve the
classification accuracy.
References
1. Hu, J., Wang, P.: Noise robustness analysis of performance for EEG-based driver
fatigue detection using different entropy feature sets. Entropy 19, 385 (2017)
2. Thomas, L.C., Gast, C., Grube, R., Craig, K.: Fatigue detection in commercial
flight operations: results using physiological measures. Procedia Manuf. 3, 2357–
2364 (2015)
3. Neri, D.F., Shappell, S.A., DeJohn, C.A.: Simulated sustained flight operations
and performance, part 1: effects of fatigue. Mil. Psychol. 4, 137–155 (1992)
4. Chaari, L., Golubnitschaja, O.: Covid-19 pandemic by the “real-time” monitoring:
the Tunisian case and lessons for global epidemics in the context of 3PM strategies.
EPMA J. (2020)
5. Sahayadhas, A., Sundaraj, K., Murugappan, M.: Electromyogram signal based
hypovigilance detection. Biomed. Res. (India) 25, 281–288 (2014)
6. Wang, F., Wang, H., Fu, R.: Real-time ECG-based detection of fatigue driving
using sample entropy. Entropy 20(3), 196 (2018)
7. Ahn, S., Nguyen, T., Jang, H., Kim, J.G., Jun, S.C.: Exploring neuro-physiological
correlates of drivers’ mental fatigue caused by sleep deprivation using simultaneous
EEG, ECG, and fNIRS data. Front. Hum. Neurosci. 10, 219 (2016)
8. Basri, C., et al.: Muscle fatigue detections during arm movement using EMG signal.
IOP Conf. Ser. Mater. Sci. Eng. 557, 012004 (2019)
9. Alom, M.Z., et al.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3), 292 (2019)