Stress commutes. For each drive, Electrocardiogram (ECG), Electromyogram (EMG),

Stress is one of the
main causes of physical and mental disorders leading to various types of
diseases. In recent two decades, stress level detection during driving in order
to avoid accidents has attracted much of researchers’ attention. However,
existing studies usually neglect the fact that stress level during driving
varies due to irregular events. Contrary to previous works, this paper
demonstrates that assuming a fixed level of stress for a long period- e.g.
while driving in highway- is unreasonable. According to the above assumption, a
novel approach for continuous stress detection is proposed based on fuzzy c-
means clustering and cluster labeling by the expert. We used fuzzy c- means clustering to specify levels of
stress instead of already different classification and labeling methods. Provided
clusters have overlap, using of fuzzy concept leads to preferable outcomes. Concurrently,
utilizing clustering results and background knowledge of data the
label of each cluster is obtained. Then proper weights are assigned to labeled
clusters.  By combining the membership values of clusters
and weights associated with the label of each cluster, a score of stress is
obtained in short time intervals. Stress
in driving dataset provide stressful conditions during real driving. The
experiments were performed on a specific route of open roads and where drivers
traverse were limited to on daily commutes. 
For each drive, Electrocardiogram (ECG), Electromyogram (EMG), foot and
hand Galvanic skin response (GSR), respiration and marker signals were acquired
from the sensors worn by the driver. Clearly the more number of physiological
signals are used, the more computational cost must be paid, so in our study,
heart rate, EMG, foot and hand GSR from mentioned dataset are used. After that,
six features consisting of the mean value of the heart rate, the mean value of
EMG, the mean values of the hand and the mean value of foot and hand GSR in
addition to mean absolute differences for hand and foot GSR are extracted for
each 10 second window (100 s window with 90% overlap) of signals. The next step
is clustering via fuzzy c-means. In this work, the data is located in 5 clusters and according to membership degree at time intervals, input signals and
background data from dataset, receives an adequate label by the expert to each
cluster. The labels of these five clusters are “very low”,
“low”, “medium”, “high”
and “very high” stress, which are respectively the least stressed to the
most stressful. Therefore, the base weight
vector is obtained as

. The weights assigned to the clusters will be a
permutation of the above-mentioned base weight vector. After assigning the weight of clusters, in each window, the membership degree
obtained by the Fuzzy c-means method is multiplied by the weight assigned to
that cluster and the resulting numbers are accumulated for the 5 clusters.
The calculated value scales to the range of 0 to 100, in
order to quantify stress. For better representation, a collection of 100
different colors in the range of dark blue to dark red of the visible spectra
will be defined by the use of “colormap” command of MATLAB. By taking the
calculated value to the range of 0 to 100, one of the mentioned colors will be
chosen. So the color will be associated to the stress value of the
corresponding window. In this paper, in addition to the qualitative
assessment of results, the correlation between the determined stress and
subjective rating scores is considered as a quantitative criterion.  The results illustrate the effectiveness of
the proposed method for improving both the precision and accuracy of stress
detection. In fact, the stress in driving dataset have imprecise labels which our
proposed systematic approach estimates the stress continuously utilizing the
background knowledge of data. Our results clearly
represent valid and efficient criteria for driving stress in each moment without
using long time window, show the continues stress from the start of experiment
till the end of it, and exaggerate individual differs and unexpected hazards
during experiment. Despite these advantages, our suggested algorithm is
automatic, fully practical and usable for the other stressful scenario.

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