An IoT-based System with Machine Learning for Detecting Drowsiness of Drivers

Authors

DOI:

https://doi.org/10.13052/jmm1550-4646.171316

Keywords:

Drowsiness, Machine-to-Machine communication, Machine Learning, pattern recognition, Internet of Things

Abstract

Drowsiness is feeling abnormally sleepy or tired. Driving is a complex psychomotor skill. Fatality rates rise as driver becomes drowsy. NHTSA accounted 91,000 motor vehicle crashes have occurred due to drowsy driving till 2017 and drowsy drivers cause 17% accidents with fatality. The IoT technology offers unprecedented opportunities to interconnect human beings as well as facilitate Machine-to-Machine (M2M) communication. The sensors and network allow all things to communicate directly with each other to share information and allow us to have an instrumented system where accurate data is readily available to make an informed optimal decision. This paper presents one such practical system for detecting drowsiness of drivers. Consequently, a system such as the one presented here can be of immense applicability in reducing the fatality rate due to traffic accidents. Usually, IoT applications, such as the one presented here, collect enormous quantity of data from the sensors and extract some sensible output, possibly using a pattern recognition algorithm. This is where Machine Learning, a branch of study under artificial intelligence, is employed. This paper presents the implementation of a system for detecting when a driver feels drowsy and sound an alarm to alert and discusses the machine learning approach adopted and the use of cloud for processing data.

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Author Biographies

Sayon Karmakar, Department of Systems Engineering, University of Arkansas at Little Rock, Arkansas, USA

Sayon Karmakar is pursuing Doctoral studies at University of Arkansas at Little Rock (UALR) under Dr. Seshadri Mohan and also a masters student at National Institute of Technology, Sikkim, India. He was a research intern in the UALR, USA under Dr. Seshadri Mohan and developed a Driver Drowsiness Detection System using multiple ML algorithms which was presented in 41st Meeting of Wireless World Research Forum (WWRF) in Aarhus University, Herning, Denmark. He has been a research coordinator to a group of students to University of Nevada, Las Vegas. Jointly with Dr. Mohan, he has given invited talks at IEEE 5G Summit held at Bihar Institute of Technology, Sindri and Indian Institute of Technology (IIT) Dhanbad and IEEE ANTS 2020 conference held by IIIT, Delhi. He holds a bachelor’s degree in electrical engineering from Siksha O Anusandhan deemed to be University, India. His current interest is concerned with “Monitoring biomarkers of drivers with medical wireless sensor networks deployed in Connected Vehicles”, “Intelligent ADAS and Adaptive Vehicular Networks: Machine Learning Perspective” and “Medical Imaging under Connected Vehicles Environment”.

Seshadri Mohan, Department of Systems Engineering, University of Arkansas at Little Rock, Arkansas, USA

Seshadri Mohan is currently a professor in Systems Engineering Department at University of Arkansas at Little Rock, where, from August 2004 to June 2013, he served as the Chair of the Department of Systems Engineering. Prior to the current position he served as the Chief Technology Officer (CTO) and Acting CEO of IP SerVoniX, where he consulted for several telecommunication firms and venture firms and served as the CTO of Telsima (formerly known as Kinera). Besides these positions, his industry experience spans a decade at New Jersey-based Telcordia (formerly Bellcore) and Bell Laboratories. Prior to joining Telcordia, he was an associate professor at Clarkson and Wayne State Universities. Dr. Mohan has authored/co-authored over 125 publications in the form of books, patents, and papers in refereed journals and conference proceedings with citations to his publications in excess of 5880. He has co-authored the textbook Source and Channel Coding: An Algorithmic Approach. He has contributed to several books, including Mobile Communications Handbook and The Communications Handbook (both CRC Press). He holds fourteen patents in the area of wireless location management and authentication strategies as well as in the area of enhanced services for wireless. He is the recipient of the SAIC Publication Prize for Information and Communications Technology. He has served or is serving on the Editorial Boards of IEEE Personal Communications, IEEE Surveys, IEEE Communications Magazine, Journal of Mobility and Cyber Security and International Journal on Wireless Personal Communications (Springer) and has chaired sessions in many international conferences and workshops. He has also served as a Guest Editor for several Special issues of IEEE Network, IEEE Communications Magazine, and ACM MONET. He served as a co-guest editor of the Feature Topic “Human Bond Communications,” that appeared in the February 2019 issue of IEEE Communications Magazine. He served as a guest editor of 2015 October IEEE Communications Feature Topic titled “Social Networks Meet Next Generation Mobile Multimedia Internet,” March 2012 IEEE Communications Feature Topic titled “Convergence of Applications Services in Next Generation Networks” as well as the June 2012 Feature Topic titled “Social Networks Meet Wireless Networks.” In April 2011, he was awarded 2010 IEEE Region 5 Outstanding Engineering Educator Award. He received the best paper award for the paper “A Multi-Path Routing Scheme for GMPLS-Controlled WDM Networks,” presented at the 4th IEEE Advanced Networks and Telecommunications Systems conference. Dr. Mohan is a co-founder of the startup IntelliNexus, LLC, the objective of which are the development of innovative adhoc vehicular networking to advance the notion of connected cars and the development of IoT and IoV applications to improve traffic safety and reduce accidents and congestion. He holds a Ph.D. degree in electrical and computer engineering from McMaster University, Canada, the Master’s degree in electrical engineering from the Indian Institute of Technology, Kanpur, India, and the Bachelor’s degree in Electronics and Telecommunications from the University of Madras, India.

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Published

2021-02-03

How to Cite

Karmakar, S., & Mohan, S. (2021). An IoT-based System with Machine Learning for Detecting Drowsiness of Drivers. Journal of Mobile Multimedia, 17(1-3), 311–328. https://doi.org/10.13052/jmm1550-4646.171316

Issue

2021: Vol 17 Iss 1-3

Section

CONASENSE