Research Article | Open Access
STRESS DETECTION USING WEARABLE DEVICES
MR. MAHESH BABU CHERUKURI, Kandhala Ananth Pal Reddy, Tiruveedula Badrinath, Surakanti Kavya Reddy, Biradar Swetha
Most of the researchers focused on detecting stress involved in a person, which causes in a person several
emotional problems like anxiety, grief, low self esteem and other mental health problems. Rece nt studies have
shown that stress can also affect the aspects of your life, including your thinking ability and physical health.
To reduce riskiness from being stress and affected with its adverse effects. Stress Lysis is a publicly available
dataset for w earable stress and affect detection. This multimodal dataset features physiological and motion
data, recorded from both a wrist and a chest worn device, of many subjects during a lab study. The following
sensor modalities are included: blood volume pulse, body temperature, electrodermal activity, heart rate,
humidity and step count. Moreover, the dataset bridges the gap between previous lab studies on stress and
emotions, by containing three different affective states (neutral = 0, stress = 1, amusement = 2). Stress detector
classifies a stressed individual from a normal one by acquiring his/her physiological signals through
appropriate sensors such as Electrocardiogram (ECG), Galvanic Skin Response (GSR) etc. These signals are
pre processed to extract the d esired features which depicts the stress level in working individuals. XGBoost,
Logistic Regression, KNN and Adaboost are investigated to classify these extracted feature set. The result
indicates feature vector with best features having a strong influence in stress identification. An attempt is
made to determine the best feature set that results in maximum classification accuracy
: Stress lysis, Multi modal dataset, XGBoost.
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