Arduino based Real Time Drowsiness and Fatigue Detection for Bikers using Helmet

Arduino based Real Time Drowsiness and Fatigue Detection for Bikers using Helmet

Table of Contents


Vehicle accidents are rapidly increasing in many countries. Among many other factors, drowsiness and fatigue are playing a major role in these accidents, and systems that can monitor it are currently being developed. Among them, Electroencephalography (EEG) proved to be very reliable. The conventional vehicle and the vision-based detection for drowsiness is very much essential only when the driver is about to sleep and every so often very late in preventing fatalities on road. This paper is specially developed to improve the safety of the bikers. The proposed system has EEG-sensors which are implemented within the helmet to detect the drowsy state of the driver. The biomedical signal from the driver’s brain is sensed by a Brain-wave sensor. This system provides real-time drowsiness and fatigue detection for the bikers by making a helmet to play a vital part with the warning platform as a miniaturized sensor and to provide a mind-machine interface (MMI) to address the challenges like drowsiness and fatigue. When the biker is detected to be in drowse state the system alerts the biker by an alarm and the motor gets slow down and stopped.

Author Keywords

  • Drowsiness,
  • Fatigue detection,
  • EEG,
  • Arduino,
  • Mind Machine interface

IEEE Keywords

  • Electroencephalography,
  • Vehicles,
  • Fatigue,
  • Accidents,
  • Real-time systems,
  • Sleep,
  • Logistics


Fatigue and drowsiness in human drivers are a serious cause of road accidents. About 10-20% of road accidents are caused by driver’s ignorance. The administrators of the “national highway traffic safety” have evaluated the rate of crashes, injuries, and fatalities to be 72,000.01, 44,000.02, and 800.03 respectively in 2013 due to the drowsiness [1]. Drowsy driving poses a major problem in road accidents as it is a violent combination of driving and sleepiness or fatigue.

The risk and dangerous results of drowsy driving are alarming. There is a great requirement of real-time driver alertness monitoring in emerging intelligent transportation systems to prevent a huge number of accidents. Our proposed system detects the abnormal state of the driver and alerts him with high-frequency alarm placed near the helmet when the driver is detected to be drowsy and it also has the mechanism to stop the Vehicle when the rider continues to stay in a drowsy state.

This proposed EEG-based MMI (Mind Machine interface) is equipped with the wireless module to acquire physiological signals and a processing unit for the same. The signal acquired from the microwave electrodes are amplified, filtered, processed, and analyzed to detect the drowsy states of the driver. It also triggers the alarm to the driver on if detects the driver’s drowsiness and prevents accidents.

Result & Discussion

This system delivers the most efficient and embedded drowsy driver detection system for bike riders using a helmet. The outcome is targeted to help common people to overcome accidents due to drowsiness and fatigue. As soon as the drowsiness of the driver is detected alarm helps to wake up the driver, the motor gets slow down to prevent major injuries. If the driver’s brain signals are in between 4-8Hz, that is the driver is in drowse state, the alarm will ring. If the driver continues to be in that state that is in our case if the alarm rings three times continuously then the motor gets slow down and then stopped.

Further Enhancement

The drowsiness detection with online warning feedback in real-time is instigated. An alternative approach parametric autoregressive (AR) modeling engaged in extracting features in the categorization of EEG and must be equipped with the capability of examining the convenience and efficiency in various frequency bands utilizing the entropies for the real-time identification [18-20]. Its inherent capacity to model the peak spectra and all-pole model resolves sharp changes in the spectra and helps to achieve high accuracy in estimation of the threshold. Nanotechnology can be used to incorporate most of the hardware that can be included in the helmet by eliminating the interference and using digital twin more controls can be added to the bike motor which prevents the driver from the accident.

About KSRA

The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.

Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.

The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.

FULL Paper PDF file:

Arduino based Real-Time Drowsiness and Fatigue Detection for Bikers using Helmet



M. Oviyaa, P. Renvitha, R. Swathika, I. J. L. Paul and S. Sasirekha




Arduino based Real-Time Drowsiness and Fatigue Detection for Bikers using Helmet

Publish in

2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India, 2020, pp. 573-577



PDF reference and original file: Click here



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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.