Driver Drowsiness Detection and Vehicle diagnostics using Android Bluetooth

  title={Driver Drowsiness Detection and Vehicle diagnostics using Android Bluetooth},
  author={R. Jadhav and Mayuri H. Godse and Supriya P. Pawar and Pallavi M. Baravkar},
  journal={International journal on innovative research in electrical, electronics, instrumentation and control engineering},
2,3,4 Abstract: Drowsiness is identified using eye blink count. The alcohol consumption is also verified during the starting process of the vehicle. Drunken driving is prevented. Continuously temperature monitoring. Eye blinks count and alcohol detection using android Bluetooth, buzzer indication if driver is not wearing seat belt. Stepper motor controls the fuel tank for drowsy person to prevent accident. GPS location indication and SMS alert in case of accident. The Growing no of fatal… Expand


Drivers drowsiness detection in embedded system
  • Tianyi Hong, Huabiao Qin
  • Computer Science
  • 2007 IEEE International Conference on Vehicular Electronics and Safety
  • 2007
This method break traditional way of drowsiness detection to make it real time, it utilizes face detection and eye detection to initialize the location of driver's eyes; after that an object tracking method is used to keep track of the eyes. Expand
Individualized drowsiness detection during driving by pulse wave analysis with neural network
This paper presents a detection method of driver's drowsiness with focus on analyzing individual differences in biological signals and performance data. We have studied biological signals of a driverExpand
Sleepiness and driving: the experience of UK car drivers
  • G. Maycock
  • Psychology, Medicine
  • Journal of sleep research
  • 1996
Accident rates of company car drivers and for those who have felt close to falling asleep at the wheel in the last year are shown to be associated with daytime sleepiness, while Snoring every night increases accident liability by about 30%). Expand
Sleepiness and driving: the experience of heavy goods vehicle drivers in the UK
  • G. Maycock
  • Psychology, Medicine
  • Journal of sleep research
  • 1997
Drivers who reported snoring regularly whilst sleeping at night or who were obese or who had a noticeably large collar size had higher accident liabilities than those not exhibiting these characteristics. Expand
Driver sleepiness and risk of serious injury to car occupants: population based case control study
Driving while feeling sleepy, driving after five hours or less of sleep, and driving between 2 am and 5 am were associated with a substantial increase in the risk of a car crash resulting in serious injury or death reduction in the prevalence of these three behaviours may reduce the incidence of injury crashes. Expand
Criteria for driver impairment
To provide an acceptable definition of driver impairment, a method to assess absolute and relative criteria was proposed to fulfil the paradoxical goal of defining impaired driving which is consistent yet adaptable to interindividual differences. Expand
Estimate of driver's fatigue through steering motion
  • Y. Takei, Y. Furukawa
  • Mathematics, Computer Science
  • 2005 IEEE International Conference on Systems, Man and Cybernetics
  • 2005
The chaos theory was applied to explain the change of steering wheel motion and a strange trajectory called attractor was found by applying the Takens' theory of embedding to find out the chaos characteristics which can indicate that a driver is fired of driving and he/she may feel fatigue. Expand
Impairment of Driving Performance Caused by Sleep Deprivation or Alcohol: A Comparative Study
The results revealed that the full sleep deprivation and alcohol group exhibited a safety-critical decline in lane-keeping performance, and both sleep-deprived groups were characterized by subjective discomfort and an awareness of reduced performance capability. Expand
The role of driver distraction in traffic crashes
Distraction is one form of inattention and it is a factor in over half of the crashes that involve some form of driver inattention. (The National Highway Traffic Safety administration estimates thatExpand
Automatic recognition of alertness and drowsiness from EEG by an artificial neural network.
The LVQ neural network gives the best classification compared with the linear network that uses WH algorithm (the worst), and the non-linear network trained with the LM rule, and it is shown that the automatic recognition algorithm is applicable for distinguishing between alert and drowsy state in recordings that have not been used for the training. Expand