TY - GEN
T1 - Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones
AU - Sano, Akane
AU - Phillips, Andrew J.
AU - Yu, Amy Z.
AU - McHill, Andrew W.
AU - Taylor, Sara
AU - Jaques, Natasha
AU - Czeisler, Charles A.
AU - Klerman, Elizabeth B.
AU - Picard, Rosalind W.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/15
Y1 - 2015/10/15
N2 - What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67-92%.
AB - What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67-92%.
KW - accelerometer
KW - big five personality
KW - machine learning
KW - mental health
KW - mobile phone
KW - personality
KW - phone call
KW - skin conductance
KW - sleep
KW - smart phone
KW - SMS
KW - stress
KW - wearable sensor
UR - http://www.scopus.com/inward/record.url?scp=84961644323&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961644323&partnerID=8YFLogxK
U2 - 10.1109/BSN.2015.7299420
DO - 10.1109/BSN.2015.7299420
M3 - Conference contribution
AN - SCOPUS:84961644323
T3 - 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
BT - 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
Y2 - 9 June 2015 through 12 June 2015
ER -