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)
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Abstract
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.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Driver drowsiness; Electromyography; Feature selection; Frequency-domain features; Statistical rank; Time-domain features |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 15 Nov 2024 02:39 |
| Last Modified: | 15 Nov 2024 02:39 |
| URI: | http://umpir.ump.edu.my/id/eprint/42665 |
| Download Statistic: | View Download Statistics |
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