Driver’s fatigue classification based on physiological signals using RNN-LSTM technique
One of the major reasons for road accidents is driver’s fatigue which causes several fatalities every year. Various studies on road accidents have proved that 20% of the accidents are caused mainly due to fatigue among drivers while driving. This paper presents the use of deep learning technique in...
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| Format: | Conference or Workshop Item |
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IEEE
2022
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| author | Rabea, Ahmed Faozi Ahmed Ahmad, Siti Anom Jantan, Sa'diah Che Soh, Azura Ishak, Asnor Juraiza Raja Adnan, Raja Nurzatul Efah Al-Qazzaz, Noor Kamal |
| author_facet | Rabea, Ahmed Faozi Ahmed Ahmad, Siti Anom Jantan, Sa'diah Che Soh, Azura Ishak, Asnor Juraiza Raja Adnan, Raja Nurzatul Efah Al-Qazzaz, Noor Kamal |
| author_sort | Rabea, Ahmed Faozi Ahmed |
| collection | UPM |
| description | One of the major reasons for road accidents is driver’s fatigue which causes several fatalities every year. Various studies on road accidents have proved that 20% of the accidents are caused mainly due to fatigue among drivers while driving. This paper presents the use of deep learning technique in classifying fatigue in drivers. By using deep neural networks, features are extracted automatically from preprocessed data of physiological signals such as electrocardiogram, heart rate, skin conductance response and body temperature. Public dataset HciLAB was used to train and validate the classification model. In this work, a comparative analysis of using Recurrent Neural Network - Long Short-term Memory (RNN-LSTM) deep learning architecture and the standard artificial neural network (ANN) was proposed and developed to classify fatigue based on the physiological features of the driver. The results revealed the superiority RNN-LSTM (98%) over standard ANN (80%), for driver fatigue classification. The proposed methods, based on RNN-LSTM deep learning architecture introduced elevated average accuracy in comparison with the standard artificial neural network. |
| first_indexed | 2024-03-06T08:39:22Z |
| format | Conference or Workshop Item |
| id | upm.eprints-37788 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T08:39:22Z |
| publishDate | 2022 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-377882023-11-06T10:21:08Z http://psasir.upm.edu.my/id/eprint/37788/ Driver’s fatigue classification based on physiological signals using RNN-LSTM technique Rabea, Ahmed Faozi Ahmed Ahmad, Siti Anom Jantan, Sa'diah Che Soh, Azura Ishak, Asnor Juraiza Raja Adnan, Raja Nurzatul Efah Al-Qazzaz, Noor Kamal One of the major reasons for road accidents is driver’s fatigue which causes several fatalities every year. Various studies on road accidents have proved that 20% of the accidents are caused mainly due to fatigue among drivers while driving. This paper presents the use of deep learning technique in classifying fatigue in drivers. By using deep neural networks, features are extracted automatically from preprocessed data of physiological signals such as electrocardiogram, heart rate, skin conductance response and body temperature. Public dataset HciLAB was used to train and validate the classification model. In this work, a comparative analysis of using Recurrent Neural Network - Long Short-term Memory (RNN-LSTM) deep learning architecture and the standard artificial neural network (ANN) was proposed and developed to classify fatigue based on the physiological features of the driver. The results revealed the superiority RNN-LSTM (98%) over standard ANN (80%), for driver fatigue classification. The proposed methods, based on RNN-LSTM deep learning architecture introduced elevated average accuracy in comparison with the standard artificial neural network. IEEE 2022 Conference or Workshop Item PeerReviewed Rabea, Ahmed Faozi Ahmed and Ahmad, Siti Anom and Jantan, Sa'diah and Che Soh, Azura and Ishak, Asnor Juraiza and Raja Adnan, Raja Nurzatul Efah and Al-Qazzaz, Noor Kamal (2022) Driver’s fatigue classification based on physiological signals using RNN-LSTM technique. In: 7th IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES 2022), 7-9 Dec. 2022, Kuala Lumpur, Malaysia. (pp. 280-285). https://ieeexplore.ieee.org/document/10079443 10.1109/IECBES54088.2022.10079443 |
| spellingShingle | Rabea, Ahmed Faozi Ahmed Ahmad, Siti Anom Jantan, Sa'diah Che Soh, Azura Ishak, Asnor Juraiza Raja Adnan, Raja Nurzatul Efah Al-Qazzaz, Noor Kamal Driver’s fatigue classification based on physiological signals using RNN-LSTM technique |
| title | Driver’s fatigue classification based on physiological signals using RNN-LSTM technique |
| title_full | Driver’s fatigue classification based on physiological signals using RNN-LSTM technique |
| title_fullStr | Driver’s fatigue classification based on physiological signals using RNN-LSTM technique |
| title_full_unstemmed | Driver’s fatigue classification based on physiological signals using RNN-LSTM technique |
| title_short | Driver’s fatigue classification based on physiological signals using RNN-LSTM technique |
| title_sort | driver s fatigue classification based on physiological signals using rnn lstm technique |
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