@inproceedings{fc109ebfcfdf4a38baa46385159a594a,
title = "Stress recognition using wearable sensors and mobile phones",
abstract = "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.",
keywords = "Accelerometer, Classification, Machine learning, Mobile phone, Skin conductance, Smart phone, Stress, Wearable sensor",
author = "Akane Sano and Picard, \{Rosalind W.\}",
year = "2013",
doi = "10.1109/ACII.2013.117",
language = "English (US)",
isbn = "9780769550480",
series = "Proceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "671--676",
booktitle = "Proceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013",
address = "United States",
note = "5th Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013 ; Conference date: 02-09-2013 Through 05-09-2013",
}