DRIVER FATIGUE AND DROWSINESS MONITORING

SYSTEM

S.Sithankatthan,Dr.P.K.Jawahar,

P.G.Student,Head Of Department

Department of ECE,

B.S.Abdur Rahman University,

Sithan1991@gmail.com,hodece@bsauniv.ac.in

Abstract- while driving in order to prevent

the accidents. Driver drowsiness detection

plays an important role in driver assistance

systems. The main idea is to detect the

drowsiness of a person This proposed

approach will be a new accident avoidance

system by reducing the complexity in the

hardware which is going to be implemented

in an automobile. The two main objectives

of this system are to measure the pulse rate

of the driver to detect his drowsiness/

fatigueness and to reduce the complexity in

developing the control application such as

speed control of a vehicle and giving an

emergency signal through SMS to the

nearest hospitals by using CAN protocol.

Keywords: pulse rate, speed control, CAN

protocol

I. INTRODUCTION

Nearly 80% of accidents are occurring due to

drowsiness and fatigue of driver. Several

researches on developing drowsiness detection

systems for drivers have been reported in the

literature and proposed in the way of a non-

intrusive monitoring. These drowsiness

detection methods can be categorised into two

major approaches. Video recognition

techniques using camera images have been

used widely in the method. This approach

analyses the images captured by cameras to

detect physical changes in drivers, such as

eyelid movement, eye gaze, yawning and head

nodding. This vision-based method is not very

accurate because it is severely affected by

environmental backgrounds, driving

conditions and driver activities. A few cases

have been studied to monitor the condition of

car drivers using non-intrusive biomedical

signal measurement to detect driver‟s fatigue

and drowsiness.

The non-intrusive biomedical signal

measurement method to detect driver

drowsiness has the following advantages

compared with video recognition technique.

The biomedical signals measured can give

significant information such as health, and

fatigue in addition to drowsiness of the driver.

However the measurement methods of

biomedical signals, such as wired connection

on brain , wearable chest belt and electrodes

located driving chair , are still uncomfortable

or inaccurate. Until now many efforts have

been focused on how to obtain the biomedical

signals under convenient and non-invasive

measurement environment. Our non-intrusive

driver fatigue and drowsiness monitoring

system is a feasible solution for comfortable

and reliable biomedical signal measurement.

II.ALGORITHM

In this paper we propose a novel

algorithm is peak detection algorithm.

Fig.1 flow chart for peak detection algorithm.

Fig 1. Shows the overall process of peak

detection algorithm. The proposed system

consists of two steps. The first step is getting

the filtered input signals .the second step is to

detect the peaks which has to lesser than the i

peaks .the peaks which are lesser than the i-1

will be detected and will used for the

detecting the next pulse if the value of pulse is

i+1 the there exists a peak otherwise the

process is stopped and the overall process is

repeated from the step one.

A.CAN PROTOCOL

The protocol system used in this proposed

system is CAN protocol. This protocol is

based on the “broadcast communication

system” which is message oriented protocol .

It defines the message contents rather than the

station and station address . every message

has message identifier, message frame which

is unique and contains the priority of message.

it is easy to add stations without modifying the

hardware and software. In emergency

conditions the message exchange within the

network .If for example engine load is

transmitted frequently with less delays rather

than the other dimensions .The priority of

exchanging the messages is made possible by

the message identifier. There are two different

types of frames are used in this system which

are “bus frame” and “extended frame”. The

CAN bus frame consists of “start of frame”,

“arbitatration field” and “remote transmission

request”, which is used to differentiate the data

frame and data request frame. The difference

between an extended frame format message

and a base frame format message is the length

of the identifier used. The 29-bit identifier is

made up of the 11-bit identifier (“base

identifier”) and an 18-bit extension (“identifier

extension”).

The distinction between CAN base

frame format and CAN extended frame format

is made by using the IDE bit, which is

transmitted as dominant in case of an 11-bit

frame, and transmitted as recessive in case of a

29-bit frame. As the two formats have to co-

exist on one bus, it is laid down which

message has higher priority on the bus in the

case of bus access collision with different

formats and the same identifier / base

identifier: The 11-bit message always has

priority over the 29-bit message .

B.WORKING PRINCIPLE

The general working principle of this

proposed system is shown in fig.2.

Fig 2. Design flow of proposed system.

The above system consists of three

sections which are named as automatic

dim/dip of headlight of the car, detecting the

drowsiness of the person and sending the

emergency messages to the nearest hospital.

The pulses are taken from the wrist of the

person. From the databases collected from the

different persons it is assured that the normal

pulse rate of person is 68-78 bpm and the

drowsiness person pulse rate will be around

58-63 bpm. From the above note the

drowsiness is detected and control applications

are done.

C.PULSE OXIMETER SENSOR

The principal advantage of optical sensors

for medical applications is their intrinsic safety

since there is no electrical contact between the

patient and the equipment. (An added bonus is

that they are also less suspect to

electromagnetic interference). This has given

rise to a variety of optical techniques to

monitor physiological parameters: for

example, the technique of Laser Doppler

velocimetry to measure red blood cell velocity.

However, in this lecture course we will

concentrate on the technique of pulse oximetry

for the non−invasive measurement of arterial

oxygen saturation in the blood (although a

second use of the technology will be discussed

right at the end of the course).

For patients at risk of respiratory

failure, it is important to monitor the efficiency

of gas exchange in the lungs, ie how well the

arterial blood is oxygenated (as opposed to

whether or not air is going in and out of the

lungs). Preferably, such information should be

available to clinicians of a continuous basis

(rather than every few hours). Both of these

requirements can be met non−invasively2 with

the technology of pulse oximetry. The

technique is now well established and is in

regular clinical use during anaesthesia and

intensive care (especially neonatal intensive

care since many premature infants undergo

some form of ventilator therapy).

III.EXPERIMENTAL RESULTS

Fig 3

Fig 4

Fig 5

IV.CONCLUSION

In this proposed system by using the

single measurement that is pulse the

corresponding control application such as

speed control system ,automatically increasing

and decreasing the intensity of the headlight of

a car and sending emergency signal to nearby

hospitals are done by using the measurement

of a pulse ,thus the complexity of hardware

which is going to be implemented on the

drivers mechanism is reduced by this system

and life of driver is also saved by this

proposed system thereby reducing the death

rate and this system will be more useful for

today generation.

REFERENCES

[1] Lim, Y.G., Kim, K.K., Park, K.S.: „ECG

measurement on a chair without conductive

contact‟, IEEE Trans. Biomed. Eng., 2006,

53,pp. 956–959.

[2] Jung, S.J., Kwon, T.H., Chung, W.Y.: „A

new approach to design ambient sensor

network or real time healthcare monitoring

system‟. Proc. IEEE SENSORS Conf., New

Zealand, 2009, pp. 576–580

[3] Eoh, H.J., Chung, M.K., Kim, S.H.:

„Electroencephalographic study of drowsiness

in simulated driven with sleep deprivation‟,

Int. J. Ind. Ergon., 2005, 35, pp. 307–320.

[4] Lin, C.T., Wu, R.C., Liang, S.F., Chao,

W.H., Chen, Y.J., Jung, T.P.: „EEG-based

drowsiness estimation for safety driving using

independent component analysis‟, IEEE Trans.

Circ. Syst., 2005, 52, pp. 2726–2738.