Sensor Based System to Detect Driver's Stress, Fatigue and Drowsiness using Data Analytic Approach

GRD Journals- Global Research and Development Journal for Engineering | Volume 4 | Issue 4 | March 2019 ISSN: 2455-5703

Sensor Based System to Monitor Driver’s Stress, Fatigue and Drowsiness using Data Analytic Approach S. Suma Assistant Professor Department of Computer Science & Engineering Valliammai Engineering College, Tamil Nadu, India J. Akash UG Student Department of Computer Science & Engineering Valliammai Engineering College, Tamil Nadu, India

R. V. Kamala Kannan UG Student Department of Computer Science & Engineering Valliammai Engineering College, Tamil Nadu, India R. Kishan UG Student Department of Computer Science & Engineering Valliammai Engineering College, Tamil Nadu, India

Abstract The level of stress for drivers will have a greater impact while driving. Driver stress may affect driver performance and create many accidents. Sometimes many innocent lives are lost because of driver stress level. Stress is something we cannot able to physically see and identify, hence different types of sensors like heartbeat, pupil dilation, blood pressure, respiration rate, skin response are used to predict stress level of the driver and driver behavior while driving. Driver behavior parameters like ECG, eye blinking, speed, steering angle, turn signal can be monitored while driving. These parameters might be helpful to observe driver’s distractions as well. In this project, to overcome the challenge we have proposed real data collection, IoT based sharing and data analytics. To obtain driver stress, we are planning to integrate heartbeat and eye blink sensors to predict the stress level and drowsiness of the driver during driving. In real time these sensors would be embedded in the driver glass. The real time data are processed from NetBeans as an excel file to R programming studio for data analysis.

Keywords- Driver, Data Analytics, Stress, Real Data Collection, Drowsiness

I. INTRODUCTION Stress can be viewed as a physiological reaction to ordinary, passionate, mental and physical difficulties. A long-haul presentation to upsetting circumstances can have negative wellbeing results. For example, the expanded danger of cardiovascular sicknesses and insusceptible framework issue. Subsequently, an auspicious pressure location can prompt frameworks for better administration.

The dimensions of pressure while driving influence the manner in which we drive and affect the probability of having a mishap. In an existing framework, there is no gadget to distinguish/anticipate driver stress and driving proficiency. Because of the absence of driver stress discovery framework, numerous mishaps/misfortunes of life occur. A piece of exploration information introduced arranges the significant hazard factors in charge of traffic mishaps as indicated by their effect as human variables (82%), vehicle factors (2.6%), street/ecological components (2.6%), and others (2.8%).

Human elements are in charge of a significant number of the traffic mishaps out and about. The information introduced in ongoing exploration article orders the real hazard factors in charge of traffic mishaps as indicated by their effect as human elements (92%). Among these, drivers' human elements comprise of subjective mistakes (40.6%), judgment blunders (34.1%), execution blunders (10.3%), and others (15%). Intellectual blunders show up in very psychological requesting circumstances in which the subjective load as seen by the driver is high and the moves made by the driver to deal with those circumstances are in numerous events not fitting. Having the capacity to identify the advancement of the driver's intellectual load and feelings of anxiety and to anticipate exceptionally requesting circumstances is vital so as to give assistance to the driver to more readily deal with these circumstances.

To detect the driver conditions, methods based on vision or physiological signals can be used. The vision-based method uses cameras and image processing to monitor the eyelid, head movement, and facial expression of the driver. However, sensitivity to environmental factors, such as lighting conditions is a major problem that must be solved. Furthermore, this method can detect a condition only after it begins to show on the face. The method based on physiological signals detects abnormal conditions by monitoring the change in these signals according to each condition.

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Sensor Based System to Monitor Driver’s Stress, Fatigue and Drowsiness using Data Analytic Approach (GRDJE/ Volume 4 / Issue 4 / 001)

II. RELATED WORKS Many researches to investigate stress level detection of a driver during real world driving experiment. This detection is based on heart rate variability (HRV) analysis which is derived from ECG signal and reflects autonomic nervous system state of the human body. The alteration of autonomic frightened device predicts the stress level of drivers in the course of driving operation and lets in a secure driving with the aid of the opportunity of an early warning. This stress, taking place during driving, is caused by diverse factors such as changing mood, bio rhythm, fatigue, boredom or disease which can prevent the driver from reaching in appropriate state for driving.

Certain other research describes drivers overloaded with information significantly increase the chance of vehicle collisions. Driver workload, which is a multidimensional variable, is measured by both performances based and subjective measurements and affected by driver age differences. Few existing computational models are able to cover these major properties of driver workload or simulate subjective mental workload and human performance at the same time. We describe a brand-new computational method in modeling driving force performance and workload a queuing network method primarily based at the queuing community concept of human performance and neuroscience discoveries.

A simulator and a field experiment were conducted to study the effects of visual and cognitive load on driving performance, and also to assess the validity on the VTI simulator as a device for analyzing the effects of distraction. It was located that visible load resulted in deteriorated lateral control and to a point decreased velocity control, even though there has been a clean effect of the drivers reducing their pace and growing the steerage hobby to be able to make amends for the expanded visual load.

III. PROPOSED SYSTEM A. Architecture Diagram

Fig. 1: Architecture Diagram

The architecture diagram involves the process of interaction between the driver, admin and an end user. The stress is a major factor for accident occurrence. In order to do this, the driver uses a wearable device in which sensors are attached. The admin monitors the driver’s details which are detected from the sensor. It is being done by generating an account for the individual driver. The driver’s data are stored in the cloud database where it is provided with the security to the driver’s records as they contain the sensitive information. With reference to the driver’s data the particular driver record is retrieved from the database. Finally, the data is processed using data analytics and the output is being predicted.

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Sensor Based System to Monitor Driver’s Stress, Fatigue and Drowsiness using Data Analytic Approach (GRDJE/ Volume 4 / Issue 4 / 001)

B. Data Flow Diagram

Fig. 2: Data Flow Diagram

C. Module Description 1) Driver The driver plays a crucial part of this module and the stress is being monitored by the wearable device from the driver and when the driver feels drowsy a music alert system is being setup to awake him from occurring accident.

2) Admin The role of admin is to maintain all records the driver on the regular basis. Admins are responsible for the day-to-day of live monitoring and uploading the details in cloud storage.

operation

3) Preprocessing In this module, the admin downloads the data from cloud and converts the text file to excel for data analysis process. Then data filtering process is done to segregate the null values.

4) Predicted Output The final output is obtained by clustering and classification techniques in which there will be a vast range of data to implement analysis part and output is obtained.

D. Naïve Bayes The Naive Bayesian classifier depends upon Bayes' hypothesis with autonomy suspicions between markers. A Naive Bayesian model is definitely not hard to work, with no tangled iterative parameter estimation which makes it particularly accommodating for sweeping datasets. In spite of its effortlessness, the Naive Bayesian classifier frequently does shockingly well and is broadly utilized in light of the fact that it regularly beats progressively refined arrangement techniques.

– P(c|x) is the back likelihood of class (target) given indicator (characteristic).

– P(c) is the earlier likelihood of class.

– P(x|c) is the probability which is the likelihood of indicator given class.

– P(x) is the earlier likelihood of indicator.

E. Naïve Bayes Classifier Use the rule

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Sensor Based System to Monitor Driver’s Stress, Fatigue and Drowsiness using Data Analytic Approach (GRDJE/ Volume 4 / Issue 4 / 001)

F. Naïve Bayes Training 1) Training in Naïve Bayes is Easy Estimate P(Y=v) as the fraction of records with Y=v

Estimate P(Xi=u|Y=v) as the fraction of records with Y=v for which X i=u

G. K-Means Clustering The primary thought is to characterize k focuses, one for each bunch. These focuses ought to be put slyly on account of the various area causes an alternate outcome. Along these lines, the better decision is to put them however much as could reasonably be expected far from one another.

'||xi - vj||' is the Euclidean separation among xi and vj. 'ci' is the quantity of information focuses on ith group. 'c' is the number of bunch focuses.

IV. OUTPUT AND RESULTS

Fig. 3: Data Set

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Sensor Based System to Monitor Driver’s Stress, Fatigue and Drowsiness using Data Analytic Approach (GRDJE/ Volume 4 / Issue 4 / 001)

This is the Data sets which is generated from the driver and classified on the basis of the real time data so machine can be fed with real time data and it can learn and apply those data to learn behaviors of drivers and avoid accidents. So with this huge data sets we can train the machine to predict the driver’s behavioral conditions. So by referring those data, drivers condition can be predicted.

And also with these data we can provide safe journey to commuters.

Fig. 4: Home Page

The admin who uses the system, sign up to specify their details such as giving their name, email-id, contact number etc... and then sign in. Once the admin is logged in and he monitors the behavior of the driver.

Fig. 5: Login Page

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Sensor Based System to Monitor Driver’s Stress, Fatigue and Drowsiness using Data Analytic Approach (GRDJE/ Volume 4 / Issue 4 / 001)

Fig. 6: Details

Fig. 7: Live Monitoring All rights reserved by www.grdjournals.com

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Sensor Based System to Monitor Driver’s Stress, Fatigue and Drowsiness using Data Analytic Approach (GRDJE/ Volume 4 / Issue 4 / 001)

Fig. 8: Predicted Output

The uploaded data is further processed by clustering and classification techniques using data analytics approach and the abnormal, normal and critical conditions are predicted for individual drivers and output is obtained.

V. FUTURE ENHANCEMENT –

Further data processing can be done by using supervised machine learning algorithm to obtain persistent and huge date processing.

VI. CONCLUSION From the above reference we can say that terabytes of huge data can be analyzed and processed by using those data efficiently.

The results in this project show that the current stress levels while driving, the driving actions taken by the user to respond to previous and recent stressors by this we can prevent accidents and save human lives. It also contains the structured, unstructured and semi-structured data in the driver’s record. In this paper, we get the driver’s information and we analyze and predict the abnormal, normal and critical conditions.

REFERENCES [1] [2] [3] [4] [5]

C. Wu and Y. Liu, “Queuing network modeling of driver workload and performance,” IEEE Trans. Intell. Transp. Syst., vol. 8, no. 3, Sep. 2007, pp. 528– 537.

M. Itoh, E. Kawakita, and K. Oguri, “Real-time estimation of driver’s mental workload using physiological indexes,” in Proc. ITS World Congr., 2010, pp.

1–11.

E. T. T. Teh, S. Jamson, and O. Carsten, “How does a lane change performed by a neighbouring vehicle affect driver workload?” in Proc. ITS World Congr., 2012, pp. 1–8.

S. Sega, H. Iwasaki, H. Hiraishi, and F. Mizoguchi, “Verification of driving workload using vehicle signal data for distraction-minimized systems on ITS,” in Proc. ITS World Congr., 2011, pp. 1–12.

J. H. Kim, Y. S. Kim, and W. S. Lee, “Real-time monitoring of driver’s cognitive distraction,” in Proc. Spring Conf. Korean Soc. Autom. Eng., May 2011, pp. 1197–1202.

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