Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection
Abstract
:1. Introduction
1.1. Driver Drowsiness Detection
1.2. Feature Selection
2. Materials and Methods
2.1. Preprocessing
2.2. Feature Extraction
2.3. Filter Method Indexes
2.3.1. Fisher Index
2.3.2. Correlation Index
2.3.3. T-test Index
2.3.4. Mutual Information Index
2.4. Fuzzy Inference System
2.5. ANFIS Training by PSO Algorithm
2.6. Support Vector Machine Classifier
3. Dataset and Experimental Setup
4. Results and Discussion
- (1)
- True Positive (TP): number of drowsy states that correctly classified as drowsy;
- (2)
- True Negative (TN): number of awake states that correctly identified as awake;
- (3)
- False Negative (FN): number of drowsy states that incorrectly identified as awake;
- (4)
- False Positive (FP): number of awake states that incorrectly identified as drowsy;
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
- Initialization. For each of the particles:
- (a)
- Initialize the position,
- (b)
- Initialize the particles best position to its initial position,
- (c)
- Calculate the performance of each particle and if initialize the global best as g = xj(0)
- Optimization loop:
- (a)
- Update the particle velocity according to (A1)
- (b)
- Update the particle position according to (A2)
- (c)
- Evaluate the performance of the particle:
- (d)
- If , update personal best:
- (e)
- If , update global best:
- At the end of the iterative process, the best solution is the final .
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Index | Time Domain Features | Descriptions |
---|---|---|
Range | Difference between minimum and maximum of signal | |
Standard Deviation | Dispersion of the data around mean value | |
Energy | Sum of the square of signal magnitude | |
Zero Crossing Rate (ZCR) | Number of steering or steering velocity direction changes per second | |
First Quartile | Middle number between the smallest number and the median of the signal in sliding window | |
Second Quartile | Median of the signal in the sliding window | |
Third Quartile | Middle value between the median and the highest value of the signal in sliding window | |
Katz Fractal Dimension (KFD) | An index for characterizing fractal patterns or sets by quantifying their complexity as a ratio of the change in detail to the change in scale. | |
Skewness | A measure for signal similarity | |
Kurtosis | Measure of tailedness of the probability distribution of a random variable | |
Sample Entropy (SamEn) | Complexity of signal in time domain based on distance in embedding dimension | |
Shannon Entropy (ShEn) | Complexity of signal in time domain based on probability function |
Index | Frequency Domain Features | Descriptions |
---|---|---|
Frequency Variability | Variance of the frequency in the defined frequency band | |
Spectral Entropy (SpEn) | Complexity of signal in frequency domain | |
Spectral Flux | Difference in the spectrum between two adjacent frames | |
Center of Gravity of Frequency (CGF) | Spectral centroid of the signal | |
Dominant Frequency | The frequency that has maximum value of the Power Spectral Density (PSD) | |
Average Value of PSD | Mean value of PSD of a sliding window in frequency domain |
Parameter | Notation | Value |
---|---|---|
Cognitive coefficient | 2 | |
Social coefficient | 2 | |
Number of population | 50 | |
Inertia weight | 0.95 | |
Random matrices 1 | , | Not constant |
True classes | |||
---|---|---|---|
Awake | Drowsy | ||
Estimated classes | Awake | TN = 24212 | FN = 538 |
Drowsy | FP = 814 | TP = 9515 | |
Samples | 25026 | 10053 |
Method | AUC | Accuracy | No. Selected Features | Selected Features |
---|---|---|---|---|
All features | 0.71 | 88.39 | 36 | All |
Fisher | 0.79 | 89.73 | 6 | , , , , , |
T-test | 0.85 | 90.21 | 5 | , , , , |
Correlation | 0.95 | 96.47 | 25 | , , , , , , , , , , , , , , , , , , , , , , , , |
Mutual information | 0.78 | 88.12 | 6 | , , , , , |
FUzzy FEature Selection (FUFES) | 0.95 | 96.41 | 10 | , , , , , , , , , |
Adaptive neuro-fuzzy feature selection | 0.97 | 98.12 | 5 | , , , , |
Study | Method | Used variables | Accuracy |
---|---|---|---|
Krajewski et al., 2009 [39] | Ensemble classification using time domain, frequency domain and state space features | Steering wheel angle, lane deviation and pedal movement | 86.1 |
McDonald et al., 2012 [40] | Random forest algorithm | Steering wheel angle | 79 |
Samiee et al., 2014 [27] | Weighted output of three trained neural networks by used variables | Steering wheel angle, lateral displacement and eye blinking | 94.63 |
Wang and Xu, 2016 [38] | Multilevel ordered logit (MOL) modeling using driver behavior and eye features metrics | Steering wheel angle, lateral displacement, speed, eye blinking and pupil diameter | 68.40 |
Li et al., 2017 [9] | Warping distance between linearized approximate entropy in sliding windows | Steering wheel angle | 78.01 |
Proposed study | Adaptive neuro-fuzzy feature selection with SVM classifier | Steering wheel angle and steering wheel velocity | 98.12 |
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Arefnezhad, S.; Samiee, S.; Eichberger, A.; Nahvi, A. Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection. Sensors 2019, 19, 943. https://doi.org/10.3390/s19040943
Arefnezhad S, Samiee S, Eichberger A, Nahvi A. Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection. Sensors. 2019; 19(4):943. https://doi.org/10.3390/s19040943
Chicago/Turabian StyleArefnezhad, Sadegh, Sajjad Samiee, Arno Eichberger, and Ali Nahvi. 2019. "Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection" Sensors 19, no. 4: 943. https://doi.org/10.3390/s19040943
APA StyleArefnezhad, S., Samiee, S., Eichberger, A., & Nahvi, A. (2019). Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection. Sensors, 19(4), 943. https://doi.org/10.3390/s19040943