@inproceedings{sano_stress_2013,
	title = {Stress {Recognition} {Using} {Wearable} {Sensors} and {Mobile} {Phones}},
	doi = {10.1109/ACII.2013.117},
	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.},
	booktitle = {2013 {Humaine} {Association} {Conference} on {Affective} {Computing} and {Intelligent} {Interaction} ({ACII})},
	author = {Sano, A. and Picard, R.W.},
	month = sep,
	year = {2013},
	keywords = {accelerometer, Accuracy, behavioral marker, binary classification, Biomedical monitoring, classification, correlation analysis, E3, emotion recognition, Feature extraction, higher-reported stress level, learning (artificial intelligence), machine learning, Mobile handsets, mobile phone, mobile phones, mobile phone usage, mobility, Mood, neurophysiology, pattern classification, physiological marker, screen on/off pattern, sensors, skin, Skin conductance, smart phone, smart phones, SMS, Stress, stress recognition, wearable computers, wearable sensor, wearable sensors, wrist sensor},
	pages = {671--676}
}