Non-contact Monitoring of Fatigue Driving Using FMCW Millimeter Wave Radar
Article No.: 3, Pages 1 - 18
Abstract
Fatigue driving is the leading cause of severe traffic accidents, which is considered as an important point of the research. Although a precise definition of fatigue is lacking, it is possible to detect the physiological characteristics of the human body to determine whether a person is fatigued, such as head shaking, yawning, and a significant drop in breathing. In our study, fatigue actions were collected first, and then the different micro-Doppler characteristics produced by human activity were used to classify and recognize the fatigue action using the fine-tuning convolution neural network (FT-CNN) model. The collected signals in the breathing mode were preprocessed to judge whether the person was fatigued according to the estimated value of the respiratory rate. Data in different environments were collected to verify the proposed method. Our results showed that the accuracy of fatigue detection can reach 91.8% in the laboratory environment and 87.3% in realistic scenarios.
References
[1]
P.-H. Ting, J.-R. Hwang, J.-L. Doong, and M.-C. Jeng. 2008. Driver fatigue and highway driving: A simulator study. Physiology & Behavior 94, 3 (2008), 448–453.
[2]
J. Hu. 2017. Comparison of different features and classifiers for driver fatigue detection based on a single EEG channel. In Computational and Mathematical Methods in Medicine.
[3]
Z.-K. Gao, Y.-L. Li, Y.-X. Yang, and C. Ma. 2019. A recurrence network-based convolutional neural network for fatigue driving detection from EEG. Chaos: An Interdisciplinary Journal of Nonlinear Science 29, 11 (2019), 113126.
[4]
J.-X. Ma, L.-C. Shi, and B.-L. Lu. 2014. An EOG-based vigilance estimation method applied for driver fatigue detection. Neuroscience and Biomedical Engineering (Discontinued) 2, 1 (2014), 41–51.
[5]
W.-L. Zheng and B.-L. Lu. 2017. A multimodal approach to estimating vigilance using EEG and forehead EOG. Journal of Neural Engineering 14, 2 (2017), 026017.
[6]
F. Wang, H. Wang, and R. Fu. 2018. Real-time ECG-based detection of fatigue driving using sample entropy. Entropy 20, 3 (2018), 196.
[7]
S. Ahn, T. Nguyen, H. Jang, J. G. Kim, and S. C. Jun. 2016. Exploring neuro-physiological correlates of drivers' mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and fNIRS data. Frontiers in Human Neuroscience 10 (2016), 219.
[8]
J.-J. Yan, H.-H. Kuo, Y.-F. Lin, and T.-L. Liao. 2016. Real-time driver drowsiness detection system based on PERCLOS and grayscale image processing. In 2016 International Symposium on Computer, Consumer and Control (IS3C’16). IEEE. 243–246.
[9]
Z. Wang, P. Shi, and C. Wu. 2020. A fatigue driving detection method based on deep learning and image processing. Journal of Physics: Conference Series 1575, 1 (2020), 012035.
[10]
Q. Gao, J. Tong, J. Wang, Z. Ran, and M. Pan. 2020. Device-free multi-person respiration monitoring using WiFi. IEEE Transactions on Vehicular Technology 69, 11 (2020), 14083–14087.
[11]
Z. Dong, M. Zhang, J. Sun, T. Cao, R. Liu, and Q. Wang. 2021. A fatigue driving detection method based on frequency modulated continuous wave radar. In 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE’21). IEEE. 670–675.
[12]
R. Chae, A. Wang, and C. Li. 2019. FMCW radar driver head motion monitoring based on Doppler spectrogram and range-Doppler evolution. In 2019 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet’19). IEEE. 1–4.
[13]
Y. Sun, T. Fei, F. Schliep, and N. Pohl. 2018. Gesture classification with handcrafted micro-Doppler features using a FMCW radar. In 2018 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM’18). IEEE. 1–4.
[14]
H. Lee, B.-H. Kim, J.-K. Park, and J.-G. Yook. 2019. A novel vital-sign sensing algorithm for multiple subjects based on 24-GHz FMCW Doppler radar. Remote Sensing 11, 10 (2019), 1237.
[15]
Y. Wang, W. Wang, M. Zhou, A. Ren, and Z. Tian. 2020. Remote monitoring of human vital signs based on 77-GHz mm-wave FMCW radar. Sensors 20, 10 (2020), 2999.
[16]
T. P. Gill. 1965. The Doppler Effect: An Introduction to the Theory of the Effect. Academic Press.
[17]
L. Cohen. 1989. Time-frequency distributions-a review. Proceedings of the IEEE 77, 7 (1989), 941–981.
[18]
V. C. Chen, F. Li, S.-S. Ho, and H. Wechsler. 2006. Micro-Doppler effect in radar: Phenomenon, model, and simulation study. IEEE Transactions on Aerospace and Electronic Systems 42, 1 (2006), 2–21.
[19]
A. H. Zuriaga, J. F. Berlanga, and C. C. Mollá. 2011. Fatigue and driving. An empirical study. Securitas Vialis 1, 3 (2011), 39–43.
[20]
W. Jia, H. Peng, N. Ruan, Z. Tang, and W. Zhao. 2018. WiFind: Driver fatigue detection with fine-grained Wi-Fi signal features. IEEE Transactions on Big Data 6, 2 (2018), 269–282.
[21]
C. Yang, X. Wang, and S. Mao. 2020. Respiration monitoring with RFID in driving environments. IEEE Journal on Selected Areas in Communications 39, 2 (2020), 500–512.
[22]
C. Jiang, J. Guo, Y. He, M. Jin, S. Li, and Y. Liu. 2020. mmVib: Micrometer-level vibration measurement with mmwave radar. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking. 1–13.
[23]
N. Aloysius and M. Geetha. 2017. A review on deep convolutional neural networks. In 2017 International Conference on Communication and Signal Processing (ICCSP’17). IEEE. 0588–0592.
[24]
Y. He, X. Li, R. Li, J. Wang, and X. Jing. 2020. A deep-learning method for radar micro-Doppler spectrogram restoration. Sensors 20, 17 (2020), 5007.
[25]
T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz. 2021. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 173 (2021), 24–49.
[26]
W. Lv, W. He, X. Lin, and J. Miao. 2021. Non-contact monitoring of human vital signs using FMCW millimeter wave radar in the 120 GHz band. Sensors 21, 8 (2021), 2732.
[27]
M. He, Y. Nian, and Y. Gong. 2017. Novel signal processing method for vital sign monitoring using FMCW radar. Biomedical Signal Processing and Control 33 (2017), 335–345.
[28]
R. Burgos-Vargas, G. Castelazo-Duarte, J. Orozco, J. Garduno-Espinosa, P. Clark, and L. Sanabria. 1993. Chest expansion in healthy adolescents and patients with the seronegative enthesopathy and arthropathy syndrome or juvenile ankylosing spondylitis. Journal of Rheumatology 20, 11 (1993), 1957–1960.
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- Non-contact Monitoring of Fatigue Driving Using FMCW Millimeter Wave Radar
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February 2024
181 pages
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Publication History
Published: 16 December 2023
Online AM: 16 September 2023
Accepted: 25 July 2023
Revised: 06 April 2023
Received: 31 October 2022
Published in TIOT Volume 5, Issue 1
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