Dual-layer ranking feature selection method based on statistical formula for driver fatigue detection of EMG signals
Electromyography (EMG) signals are one of the most studied inputs for driver drowsiness detection systems. As the number of EMG features available can be daunting, finding the most significant and minimal subset features is desirable. Hence, a simplified feature selection method is necessary. This w...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
International Information and Engineering Technology Association
2022
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/42665/1/Dual-layer%20ranking%20feature%20selection%20method%20based%20on%20statistical%20formula.pdf http://umpir.ump.edu.my/id/eprint/42665/ https://doi.org/10.18280/ts.390335 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1831530662861996032 |
|---|---|
| author | Faradila, Naim Mahfuzah, Mustafa Norizam, Sulaiman Zarith Liyana, Zahari |
| author_facet | Faradila, Naim Mahfuzah, Mustafa Norizam, Sulaiman Zarith Liyana, Zahari |
| author_sort | Faradila, Naim |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Electromyography (EMG) signals are one of the most studied inputs for driver drowsiness detection systems. As the number of EMG features available can be daunting, finding the most significant and minimal subset features is desirable. Hence, a simplified feature selection method is necessary. This work proposed a dual-layer ranking feature selection algorithm based on statistical formula f EMG signals for driver fatigue detection. In the beginning, in the first layer, 21 filter algorithms were calculated to rank 47 sets of EMG features (25 time-domain and 9 frequency-domain) and applied to six classifiers. Then, in the second layer, all the ranks were re-ranked based on the statistical formula (average, median, mode and variance). The classification performance of all rankings was compared along with the number of features. The highest classification accuracy achieved was 95% for 12 features using the Average Statistical Rank (ASR) and LDA classifier. It is conclusive that a combination of features from the time domain and frequency domain can deliver better performance compared to a single domain feature. Concurrently, the statistical rank ASR performed better than the single filter rank by reducing the number of features. The proposed model can be a benchmark for the enhanced feature selection method for EMG driver fatigue signal. |
| format | Article |
| id | my.ump.umpir.42665 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2022 |
| publisher | International Information and Engineering Technology Association |
| record_format | eprints |
| spelling | my.ump.umpir.426652024-11-15T02:39:38Z http://umpir.ump.edu.my/id/eprint/42665/ Dual-layer ranking feature selection method based on statistical formula for driver fatigue detection of EMG signals Faradila, Naim Mahfuzah, Mustafa Norizam, Sulaiman Zarith Liyana, Zahari T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Electromyography (EMG) signals are one of the most studied inputs for driver drowsiness detection systems. As the number of EMG features available can be daunting, finding the most significant and minimal subset features is desirable. Hence, a simplified feature selection method is necessary. This work proposed a dual-layer ranking feature selection algorithm based on statistical formula f EMG signals for driver fatigue detection. In the beginning, in the first layer, 21 filter algorithms were calculated to rank 47 sets of EMG features (25 time-domain and 9 frequency-domain) and applied to six classifiers. Then, in the second layer, all the ranks were re-ranked based on the statistical formula (average, median, mode and variance). The classification performance of all rankings was compared along with the number of features. The highest classification accuracy achieved was 95% for 12 features using the Average Statistical Rank (ASR) and LDA classifier. It is conclusive that a combination of features from the time domain and frequency domain can deliver better performance compared to a single domain feature. Concurrently, the statistical rank ASR performed better than the single filter rank by reducing the number of features. The proposed model can be a benchmark for the enhanced feature selection method for EMG driver fatigue signal. International Information and Engineering Technology Association 2022-06 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/42665/1/Dual-layer%20ranking%20feature%20selection%20method%20based%20on%20statistical%20formula.pdf Faradila, Naim and Mahfuzah, Mustafa and Norizam, Sulaiman and Zarith Liyana, Zahari (2022) Dual-layer ranking feature selection method based on statistical formula for driver fatigue detection of EMG signals. Traitement du Signal, 39 (3). pp. 1079-1088. ISSN 0765-0019. (Published) https://doi.org/10.18280/ts.390335 https://doi.org/10.18280/ts.390335 |
| spellingShingle | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Faradila, Naim Mahfuzah, Mustafa Norizam, Sulaiman Zarith Liyana, Zahari Dual-layer ranking feature selection method based on statistical formula for driver fatigue detection of EMG signals |
| title | Dual-layer ranking feature selection method based on statistical formula for driver fatigue detection of EMG signals |
| title_full | Dual-layer ranking feature selection method based on statistical formula for driver fatigue detection of EMG signals |
| title_fullStr | Dual-layer ranking feature selection method based on statistical formula for driver fatigue detection of EMG signals |
| title_full_unstemmed | Dual-layer ranking feature selection method based on statistical formula for driver fatigue detection of EMG signals |
| title_short | Dual-layer ranking feature selection method based on statistical formula for driver fatigue detection of EMG signals |
| title_sort | dual-layer ranking feature selection method based on statistical formula for driver fatigue detection of emg signals |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/42665/1/Dual-layer%20ranking%20feature%20selection%20method%20based%20on%20statistical%20formula.pdf http://umpir.ump.edu.my/id/eprint/42665/ https://doi.org/10.18280/ts.390335 https://doi.org/10.18280/ts.390335 |
| url_provider | http://umpir.ump.edu.my/ |