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Survey on stress detection while driving

Survey on Stress detection while driving

Anoop Mathew

Student of DBIT

University of Mumbai

Email: anoopperingalloor@gmail.com

Benjo Francis

Student of DBIT

University of Mumbai

Email: benjofrancis007@gmail.com

This paper provides a comparative analysis of five cases

of ways to detect stress while driving that have been widely

recognized as successful. Society will experience a rapidly

aging population over the next few decades, we still need to

maintain safety and security in our society. To successfully

achieve this goal, it is essential for us to predict and reduce

human errors and mishaps as far as is possible. In order

to reduce human errors, there have been many research pa-

pers published about various techniques to monitor stress or

fatigue while driving . This paper suggests factors associ-

ated with successful detection of stress , particularly in real

time driving and simulated driving environment. The paper

concludes with a discussion that the early detection of this

situation is very much necessary to improve awareness and

performance in drivers, which are important for road and

traffic safety we therefore suggest the best method to detect

stress at the earliest . We also took a survey from 16 drivers

, regarding the main reason for having stress while driving

, about 9 to 10 drivers suggest the stress is caused due to

the ongoing metro construction work , traffic and speeding 2

Wheeler’s .

1 Detailed Study of the papers

In one of the Paper, It presents a methods for collect-

ing and analyzing physiological data during real-world driv-

ing tasks to determine relative stress level among drivers.

Electrocardiogram, electromyogram, skin conductance”,and

respiration were used. Analysis 1: Data during the rest”,

highway, and city driving conditions is used to differenti-

ate three levels of driver stress with an accuracy of over 97

percentage across multiple drivers and driving days. Anal-

ysis 2: Compared continuous features, calculated at 1-s in-

tervals throughout the complete drive, with a metric of ob-

servable stressors created by independent coders from video-

tapes. Results show that, for most the drivers studied, the

skin conductivity and heart rate metrics are most closely cor-

related with driver stress level.

The subject wore five physiologiacal sensors , an elec-

trocardiogram on the chest , an electromyogram on the left

shoulder , a chest cavity expansion respiration sensor around

the diaphragm , and tweo skin conductivity sensores , one

on the left hand and one on the left foot . The sensors were

attached to a computer in the rear of the vehicle [1]

Fig. 1

In this paper they have focused on ECG monitoring, that can

now be performed with minimally wearable patches and sen-

sors.They have included three stress levels to determine the

state: low stress level, medium stress level and high stress

level . Using machine learning algorithms from the ECG

signals alone, we could achieve 88.24 percentage accuracy

in detecting the three classes of stress.[2]

In one of the paper , it investigaes drivers condition or be-

havior using Heart Rate and Breathing rate .The Heart Rate

and Breathing Rate signals are acquired with an adrenergic

sensor connected to a chest strap that is worn underneath the

drivers clothing. High level of stress compromises decision-

making skills of a driver, thus his performance and awareness

drastically decrease .As first step of our research, we present

the preliminary results we obtained using machine-learning-

Other essay:   Stress

based processing of publicly available HR and BR data , ac-

quired with wearablesensors during driving task . Experi-

ments have been conducted on a publicly available dataset of

driving simulated data. The classification problem is formu-

lated as a binary problem: stress vs no-stress.[3]

Fig. 2: In the above figure Comparison between the original signal

of a given subject (test set) and the distribution of the signals of the

rest of the subjects (training set). (a)HR of no-stress conditions. (b)

HR of stress conditions. (a) BR of no-stress conditions. (b) BR of

stress conditions

In this paper , Salivary amylase is used as a biomarker, as it

is considered to be one of theindicators of sympathetic ner-

vous activity. There were 20 healthy female subjects in their

early twenties were considered in this study. The time-course

change of their salivary amylase activity (sAMY) is analyzed

before and during the driving. In parallel, subjective evalua-

tion using a questionnaire and measurement of oculomotor (

motion of the eyes ) angle are conducted on each subject. A

psychological effect of motor-vehicle driving that could not

be easily detected by a subjective evaluation was rapidly and

quantitatively evaluated by a biomarker in saliva. Moreover”,

the results suggested that operation of an non-driving-related

device might induce a significant reduction in the drivers ca-

pacity to concentrate on driving

Fig. 3: Driving units and measurement

Study 1: Firstly, three electrodes are placed on the subject

for the measurement of the oculomotor angle. Then, subjects

answer the questions identified in the questionnaire regard-

ing psychological state (Q1). Initially, each subject takes a

sitting position for 5 min in order to measure the individual

baseline (A 1-3 , baseline period, Fig.2). The minimum value

is set as the sAMY during the base line period (AMY base ).

Next, the subjects drive for 21 min using the simulator, and

sAMY is measured every 3 min (7 times) . The oculomo-

tor angle is measured continuously during the driving period.

Immediately after completion of the measurement, the sub-

ject fills in the psychological state questionnaire (Q2). The

subjects are instructed to drive a car in the center of the lane

as much as they possibly can. Study 2: In order to investi-

gate the effects of operation of a device, which is not directly

related to driving a motor-vehicle, the subjects are instructed

to input digit on the touch panel of the car navigation device”,

which is installed in the driving unit (non-driving-related de-

vice). With a method similar to Study 1, the subjects simul-

taneously perform two tasks, driving and inputting a number

on the car navigation device and sAMY and other parameters

are measured.[4]

In this paper, they intends to understand stress level detection

of a driver during real world driving experiment. This detec-

tion is based on heart rate variability (HRV) analysis which

is derived from ECG signal and reflects autonomic nervous

system state of the human body. In our study, the ECG sig-

nal of the driver is extracted and pre-processed in order to

perform the HRV analysis.

This analysis is accomplished us-ing one of the domain anal-

ysis approach such as time, frequency, time-frequency or

non-linear methods including Wavelet and STFT. After HRV

analysis, several parameters are extracted to build a vector

of features for the classification phase. Our system archi-

tecture , It includes three main phases including data anal-

ysis, feature Extraction and classification.The first step in

Other essay:   Effects of nadi- shodhana pranayama on depression, anxiety, stress and peak expiratory f

our analysis phase is the preprocessing which includes the

filtering of raw ECG Signal from all noise sources using a

bandpass filter The second step is detecting the R peak which

indicates the most visible and highest peak of the ECG sig-

nal. Therefore the filtered signal is passed through different

phases such as derivative, squaring and window integration.

In the last step of the analysis phase it is made to derive the

heart rate variability (HRV) which is the beat-to-beat alter-

ations in heart rate (HR). Under the resting conditions of the

testers, ECG of healthy individuals exhibits periodic varia-

tion in R-R intervals

Out of all Classifiers , Using SVM-RBF classifer stress de-

tection could be predicted with an accuracy of 83% . This

also points out therobust-ness of ECG biometric as an accu-

rate physiological indicator of a driver state. Results suggest

that the QRS complex are dominant in any case regardless

of each driver’s physiological driving conditions experienced

by the automotive drivers and can be used as an accurate

non invasive biometric feature without using the whole ECG

morphology. In future work, it is suggested to differentiate

the driver stress into three different levels low, medium and

high level using more samples and identifying significant pa-

rameters .[5]

2 Analysis Review

From the 5 papers that we compared on the basis of their

performance”,efficiency”,technology used, we suggest that the

method suggested in the paper Machine Learning for Stress

Detection from ECG Signals in Automobile Drivers by Ke-

shan, Parimi and Bichindaritz[2] is more efficient compared

to other papers. In this paper they use modern method

like machine learning for detecting stress. They also ob-

tain their results by using minimum number of wearable

sensors. The result they obtain for stress detection while

driving are in 3 levels;low, medium, high. According the

paper we did study on, they got an efficiency of 100%

for low and high level stress.And 88.24% accuracy for

medium level stress.The ECG signals of drivers stress used

in this study were obtained from MIT-BIH PhysioNet Multi-

parameter Database.The data from 17 participating drivers

were taken and the database has eight types of raw data

time stamp, ECG, electromyogram(EMG), foot galvanic

skin response(GSR), hand GSR, intermittent heart rate(IHR)”,

marker, and respiration. The ECG signals of drivers that

were used in the system for detecting stress were obtained

from MIT-BIH PhysioNet Multi-parameter Database. The

database consisted of 8 set of data; ECG, time stamp”,

intermittent heart rate (IHR), foot galvanic skin response

(GSR)”,hand GSR, marker, and respiration, electromyogram

(EMG). For feature extraction they have extracted 14 differ-

ent features for analysis which include: Average Difference

Beats, Average Beats, Average QR interval and more. They

used WEKA as a classification tool to classify these extracted

features. It consists of many classifiers offered by weka”,

out of which 10 algorithms from varied types were selected

for classification to perform their comparative study, namely”,

IBK (K-nearest- neighbors), Nave Bayes, RandomForest”,

Multilayer Perceptron, SMO (support-vector machine), Lo-

gistic Regression, ZeroR, J48 (decision tree), IB1 (1-nearest

neighbor) and RandomTree. In this paper even though they

tried using different methods to get maximum efficiency”,

they obtained the maximum efficiency using Naive Bayes

algorithm. It gave 100% efficience for low and high stress

levels. Where as in other algorithms like J48 with 98% effi-

cency for the above cases.

Other essay:   Car driving assistance and monitoring system

3 Conclusion

In this paper an analysis of five papers related to stress de-

tection while driving is proposed .This paper shows the ef-

ficency of each stress detection technique .[1] In the future”,

we may want vehicles to be more intelligent and responsive”,

managing information delivery in the context of the drivers

situation. Physiological sensing is one method of accom-

plishing this goal. This study tested the applicability of phys-

iological sensing for determining a drivers overall stress level

in a real environment . The results showed that there could

be three stress levels that could be recognized with an over-

all accuracy of 97.4 percentage .[2] Using machine learning

algorithms from the ECG signals alone, we could get 88.24

percentage accuracy in detecting the three classes of stress:

low, medium and high. Also, we have obtained a perfect

100 percentage accuracy in detecting high stress levels with

NaiveBayes.[3] Results demonstrated that both HR and BR

can be used for stress prediction and that their combination

is more robust than each signal considered separately. The

accuracy reached by the best combination is about 70 per-

centage We demonstrated that the accuracy level which can

be obtained using only HR and BR sensors is very interest-

ing, therefore they can be considered very promising for the

integration into a machine-learning-based driver monitoring

system. [4] Evaluation of driver stress using a biomarker is

considered to be very useful to improve the safety and se-

curity of motor-vehicle drivers by quantification of driving-

induced stress.Moreover, the results suggested that operation

of an non-driving-related device might induce a significant

reduction in the drivers capacity to concentrate on driving

.[5] ECG signal converted into HRV for analysis reflects the

response of autonomic nervous system and allows to predict

stress level of the driver which will ensure to provide better

security for automotive drivers in order to avoid car thefts

and to recognize and identify the automotive drivers in dif-

ferent physiological driving conditions . SVM with a RBF

kernel presented the best results giving the highest rates of

correct prediction .In future work, the paper is suggested to

differentiate driver’s stress level into three different levels

low, medium and high level using more samples and iden-

tifying significant parameters to realize the purpose.

4 References

[1] Jennifer A. Healey and Rosalind W. Picard, ”Detecting

Stress During Real-World Driving Tasks Using Physiologi-

cal Sensors ””,IEEE Transactions on Intelligent Transporta-

tion System, Vol. 6, No. 2, 156 – 166, June 2005

[2] I. Bichindaritz”,N. Keshan, and P. V. Parimi “,”Machine

Learning for Stress Detection from ECG Signals in Automo-

bile Drivers ””,2015 IEEE International Conference on Big

Data (Big Data) “,29 Oct.-1 Nov. 2015

[3] Paolo Napoletano and Stefano Rossi “,”Combining heart

and breathing rate for car driver stress recognition ”, Interna-

tional Conference on Consumer Electronics – Berlin (ICCE –

Berlin)

[4] M. Yamaguchi, J. Wakasugi, J. Sakakima “,”Evaluation

of Driver Stress Using Biomarker in Motor-vehicle Driving

Simulator ”, proceedings of the 28th IEEE EMBS Annual

International Conference New York City”,Aug 30-Sept 3.

[5] Nermine Munla, Mohamad Khalil, Ahmad Shahin and

Azzam Mourad “,” Driver Stress Level Detection Using HRV

Analysis ””,2015 International Conference on Advances in

Biomedical Engineering (ICABME), 16-18 Sept. 2015.

Detailed Study of the papers

Analysis Review

Conclusion

References

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