TY - GEN
T1 - Stress recognition using wearable sensors and mobile phones
AU - Sano, Akane
AU - Picard, Rosalind W.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Accelerometer
KW - Classification
KW - Machine learning
KW - Mobile phone
KW - Skin conductance
KW - Smart phone
KW - Stress
KW - Wearable sensor
UR - http://www.scopus.com/inward/record.url?scp=84893329956&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893329956&partnerID=8YFLogxK
U2 - 10.1109/ACII.2013.117
DO - 10.1109/ACII.2013.117
M3 - Conference contribution
AN - SCOPUS:84893329956
SN - 9780769550480
T3 - Proceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013
SP - 671
EP - 676
BT - Proceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013
T2 - 2013 5th Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013
Y2 - 2 September 2013 through 5 September 2013
ER -