Abstract
Fatigue detection in today’s era has become an important asset as the death rate of India is at its peak, and the reason for it is road accidents. It is very important to identify driver’s drowsiness in the early stages, for minimizing the damage and preventing accidents. Drowsiness can be detected considering parameters like facial expressions, human physiological signals and vehicular parameters. It is possible to detect the state of driver’s fatigue with the development of the technology of IoT, machine learning and big data analytics. This paper focuses on all the techniques and processes involved in the fatigue detection, which includes all the sensors that can be used like heartbeat sensor, temperature sensor, eye blink sensor, etc. As the data collected from sensors is huge, so it can be termed as big data, and for processing this data, big data and machine learning techniques are used which are discussed here.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rolison, J.J., Regev, S., Moutari, S., Feeney, A.: What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers’ opinions, and road accident records. Accid. Anal. Prev. 115, 11–24 (2018)
Ramzan, M., Khan, H.U., Awan, S.M., Ismail, A., Ilyas, M., Mahmood, A.: A survey on state-of-the-art drowsiness detection techniques. IEEE Access 7, 61904–61919 (2019)
McDonald, A.D., Lee, J.D., Schwarz, C., Brown, T.L.: A contextual and temporal algorithm for driver drowsiness detection. Accid. Anal. Prev. 113, 25–37 (2018)
Purnamasari, P.D., Hazmi, A.Z.: Heart beat based drowsiness detection system for driver. In: 2018 International Seminar on Application for Technology of Information and Communication, pp. 585–590. IEEE (2018)
Arunasalam, M., Yaakob, N., Amir, A., Elshaikh, M., Azahar, N.F.: Real-time drowsiness detection system for driver monitoring. In: IOP Conference Series: Materials Science and Engineering, vol. 767, p. 012066. IOP Publishing (2020)
Chowdhury, A., Shankaran, R., Kavakli, M., Haque, M.M.: Sensor applications and physiological features in drivers’ drowsiness detection: a review. IEEE Sens. J. 18(8), 3055–3067 (2018)
Bharadwaj, V., Chellappa, Y., Joshi, N.N.: Driver fatigue detection system. In: IEEE International Conference on Signal and Image Processing (ICSIP), pp. 655–660 (2016)
Charniya, N.N., Nair, V.R.: Drunk driving and drowsiness detection. In: 2017 International Conference on Intelligent Computing and Control (I2C2), pp. 1–6. IEEE (2017)
Li, Z., Yang, Q., Chen, S., Zhou, W., Chen, L., Song, L.: A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data. Int. J. Distrib. Sens. Netw. 15(9), 1550147719872452 (2019)
Ziebinski, A., Cupek, R., Grzechca, D., Chruszczyk, L.: Review of advanced driver assistance systems (ADAS). In: AIP Conference Proceedings, vol. 1906, p. 120002. AIP Publishing LLC (2017)
Lakshmi Priya, B., Prithviraj, M., Baraniraj, C., Duraikannu, P.: Accident prevention system using driver drowsiness. Int. J. Innov. Sci. Res. Technol. 3(7), 182–186 (2018)
Boedinoegroho, H., Rahardjo, A.K., Kurniawan, A., Purnama, I.K.E.: Development of fatigue detection device based on IR-UWB and optic sensor to driver. In: 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), pp. 1–4. IEEE (2019)
Erins, M., Minejeva, O., Kivlenieks, R., Lauznis, J., et al.: Feasibility study of physiological parameter registration sensors for non-intrusive human fatigue detection system. In: Proceedings of the 18th International Scientific Conference Engineering for Rural Development, Jelgava, Latvia, pp. 22–24 (2019)
Yadranjiaghdam, B., Pool, N., Tabrizi, N.: A survey on real-time big data analytics: applications and tools. In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 404–409. IEEE (2016)
Vassakis, K., Petrakis, E., Kopanakis, I.: Big data analytics: applications, prospects and challenges. In: Mobile Big Data, pp. 3–20. Springer, Berlin (2018)
Babar, M., Arif, F.: Real-time data processing scheme using big data analytics in internet of things based smart transportation environment. J. Ambient Intell. Humanized Comput. 10(10), 4167–4177 (2019)
Gürcan, F., Berigel, M.: Real-time processing of big data streams: lifecycle, tools, tasks, and challenges. In: 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–6. IEEE (2018)
Syed, D., Zainab, A., Ghrayeb, A., Refaat, S.S., Abu-Rub, H., Bouhali, O.: Smart grid big data analytics: survey of technologies, techniques, and applications. IEEE Access 9, 59564–59585 (2020)
Dhankhar, A., Solanki, K.: A comprehensive review of tools and techniques for big data analytics. Int. J. Emerg. Trends Eng. Res. 7(11), 556–562 (2019)
Syed, L., Jabeen, S., Manimala, S., Alsaeedi, A.: Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques. Future Gener. Comput. Syst. 101, 136–151 (2019)
Batool, M., Jalal, A., Kim, K.: Sensors technologies for human activity analysis based on SVM optimized by PSO algorithm. In: 2019 International Conference on Applied and Engineering Mathematics (ICAEM), pp. 145–150. IEEE (2019)
Shanthamallu, U.S., Spanias, A., Tepedelenlioglu, C., Stanley, M.: A brief survey of machine learning methods and their sensor and iot applications. In: 2017 8th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–8. IEEE (2017)
Herrera-Alcántara, O., Barrera-Animas, A., González-Mendoza, M., Castro-Espinoza, F.: Monitoring student activities with smartwatches: on the academic performance enhancement. Sensors 19(7), 1605 (2019)
Kanjo, E., Younis, E.M.G., Ang, C.S.: Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection. Inf. Fusion 49, 46–56 (2019)
Kanjo, E., Younis, E.M.G., Sherkat, N.: Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach. Inf. Fusion 40, 18–31 (2018)
Gloor, P., Colladon, A.F., Giacomelli, G., Saran, T., Grippa, F.: The impact of virtual mirroring on customer satisfaction. J. Bus. Res. 75, 67–76 (2017)
Wu, E.Q., Peng, X.Y., Zhang, C.Z., Lin, J.X., Sheng, R.S.F.: Pilots’ fatigue status recognition using deep contractive autoencoder network. IEEE Trans. Instrum. Meas. 68(10), 3907–3919 (2019)
Sun, J., Zhang, R., Jin, X., Zhang, Y.: Securefind: secure and privacy-preserving object finding via mobile crowdsourcing. IEEE Trans. Wirel. Commun. 15(3), 1716–1728 (2015)
ud din Tahir, S.B., Jalal, A., Batool, M.: Wearable sensors for activity analysis using SMO-based random forest over smart home and sports datasets. In: 2020 3rd International Conference on Advancements in Computational Sciences (ICACS), pp. 1–6 (2020)
Verikas, A., Gelzinis, A., Bacauskiene, M.: Mining data with random forests: a survey and results of new tests. Pattern Recogn. 44(2), 330–349 (2011)
Dong, L., Du, H., Mao, F., Han, N., Li, X., Zhou, G., Zheng, J., Zhang, M., Xing, L., Liu, T., et al.: Very high resolution remote sensing imagery classification using a fusion of random forest and deep learning technique-subtropical area for example. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 13, 113–128 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Deshmukh, G., Khuspe, A., Kadam, R., Kamble, A., Phalke, A. (2023). Survey on Driver Fatigue Detection Using Sensors, Big Data Analytics and Machine Learning Techniques. In: Choudrie, J., Mahalle, P., Perumal, T., Joshi, A. (eds) ICT with Intelligent Applications. Smart Innovation, Systems and Technologies, vol 311. Springer, Singapore. https://doi.org/10.1007/978-981-19-3571-8_10
Download citation
DOI: https://doi.org/10.1007/978-981-19-3571-8_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-3570-1
Online ISBN: 978-981-19-3571-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)