Stress and sleep quality estimation with the use of smart Wearable sensors

Topics: Stress, Sympathetic nervous system, Root mean square Pages: 7 (3149 words) Published: September 26, 2014
Stress and sleep quality estimation with the use of smart
Wearable sensors
1

Harsha Puranik
Harshpuranik44@gmail.com
1

2

S.S. Kataria
nileshpatni@indiatimes.com

Institute Of Knowledge College Of Engineering, Pune
University of Pune
2
Amrutvahini College of Engineering, Sangamner
University of Pune

Abstract: The stress and poor sleep quality of
a person may be used as two of several
components for predicting the onset of mental
health problems, in particular depression.
Continuous stress monitoring may help users
better understand their stress patterns and
provide physicians with more reliable data for
interventions. Previously, studies on mental
stress detection were limited to a laboratory
environment where participants generally
rested in a sedentary position. However, it is
impractical to exclude the effects of physical
activity while developing a pervasive stress
monitoring application for everyday use. The
physiological responses caused by mental
stress can be masked by variations due to
physical activity.We present an activity-aware
mental
stress
detection
scheme.
Electrocardiogram (ECG), galvanic skin
response (GSR), and accelerometer data were
gathered from 20 participants across three
activities: sitting, standing, and walking. For
each activity, we gathered baseline
physiological
measurements
and
measurements while users were subjected to
mental stressors. The activity information
derived from the accelerometer enabled us to
achieve 92.4% accuracy of mental stress
classification for 10-fold cross validation and
80.9%
accuracy
for
between-subjects
classification.Ergonomic smart sensors that
can determine the heart rate variations related

to stress and the variability of sleep may
provide unique insights to the coping behavior
of stressed people. Rather than relying on
wearable computers, a single smart miniature
sensor that is worn 24/7 should perform the
complex embedded recognition tasks while
meeting difficult battery life, wireless
communications and ergonomic constraints.
The development and testing of such a smart
sensor
is
described
focusing
on
implementation within distributed intelligence
based architecture. The manner in which the
user’s heart rate and the user’s physical
motion is used to measure stress and sleep
quality is explained.
Key words: Mental stress, electrocardiogram,
galvanic skin response, Ergonomic smart
sensors physical activity, heart rate variability,
decision tress, Bayes net, support vector
machine, stress classifier.
1 INTRODUCTION
Stress is a physiological response to the
mental, emotional, or physical challenges that
we encounter. Immediate threats provoke the
body's fight or flight" response, or acute stress
response [5]. The body secretes hormones,
such as adrenaline, into the bloodstream to
intensify concentration. There are also many
physical changes, such as increased heart rate
and quickened reflexes. Under healthy

conditions, the body returns to its normal state
after
dealing
with
acute
stressors.
Unfortunately, many of the stressors in
modern life are on-going. Chronic stress can
be detrimental to both physical and mental
health. It is a risk factor for hypertension and
coronary artery disease [22, 12]. Other
physical disorders, including irritable bowel
syndrome (IBS), gastroesophageal reflux
disease (GERD), and back pain, may be
caused or exacerbated by stress [16]. Chronic
stress also plays a role in mental illnesses,
such as generalized anxiety disorder and
depression [11].
Continuous monitoring of an individual's
stress levels is essential for understanding and
managing personal stress. A number of
physiological markers are widely used for
stress assessment, including: galvanic skin
response, several features of heart beat
patterns, blood pressure, and respiration
activity [31, 15]. Fortunately, miniaturized
wireless devices are available to...

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