Stress Recognition Using Wearable Sensors and Mobile Phones

In this study, we aim to find physiological or behavioral markers for stress. We collected 5 days of data for 18 participants: a wrist sensor (accelerometer and skin conductance), mobile phone usage (call, short message service, location and screen on/off) and surveys (stress, mood, sleep, tiredness...

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Bibliographic Details
Main Authors: Sano, Akane, Picard, Rosalind W.
Format: Conference Proceeding
Language:English
Subjects:
accelerometer
Accuracy
Biomedical monitoring
classification
Feature extraction
machine learning
Mobile handsets
mobile phone
Mood
Skin
skin conductance
smart phone
Stress
wearable sensor
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Summary:In this study, we aim to find physiological or behavioral markers for stress. We collected 5 days of data for 18 participants: a wrist sensor (accelerometer and skin conductance), mobile phone usage (call, short message service, location and screen on/off) and surveys (stress, mood, sleep, tiredness, general health, alcohol or caffeinated beverage intake and electronics usage). We applied correlation analysis to find statistically significant features associated with stress and used machine learning to classify whether the participants were stressed or not. In comparison to a baseline 87.5% accuracy using the surveys, our results showed over 75% accuracy in a binary classification using screen on, mobility, call or activity level information (some showed higher accuracy than the baseline). The correlation analysis showed that the higher-reported stress level was related to activity level, SMS and screen on/off patterns.
ISSN:2156-8103
2156-8111
DOI:10.1109/ACII.2013.117