Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

Akane Sano; Andrew J. Phillips; Amy Z. Yu; Andrew W. McHill; Sara Taylor; Natasha Jaques; Charles A. Czeisler; Elizabeth B. Klerman; Rosalind W. Picard

doi:10.1109/BSN.2015.7299420

Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

Akane Sano, Andrew J. Phillips, Amy Z. Yu, Andrew W. McHill, Sara Taylor, Natasha Jaques, Charles A. Czeisler, Elizabeth B. Klerman, Rosalind W. Picard

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

153 Scopus citations

Abstract

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%.

Original language	English (US)
Title of host publication	2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467372015
DOIs	https://doi.org/10.1109/BSN.2015.7299420
State	Published - Oct 15 2015
Externally published	Yes
Event	12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015 - Cambridge, United States Duration: Jun 9 2015 → Jun 12 2015

Publication series

Name	2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015

Conference

Conference	12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
Country/Territory	United States
City	Cambridge
Period	6/9/15 → 6/12/15

Keywords

SMS
accelerometer
big five personality
machine learning
mental health
mobile phone
personality
phone call
skin conductance
sleep
smart phone
stress
wearable sensor

ASJC Scopus subject areas

Computer Networks and Communications
Health Informatics
Instrumentation
Signal Processing

Access to Document

10.1109/BSN.2015.7299420

Cite this

Sano, A., Phillips, A. J., Yu, A. Z., McHill, A. W., Taylor, S., Jaques, N., Czeisler, C. A., Klerman, E. B., & Picard, R. W. (2015). Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones. In 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015 Article 7299420 (2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BSN.2015.7299420

Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones. / Sano, Akane; Phillips, Andrew J.; Yu, Amy Z. et al.
2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7299420 (2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sano, A, Phillips, AJ, Yu, AZ, McHill, AW, Taylor, S, Jaques, N, Czeisler, CA, Klerman, EB & Picard, RW 2015, Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones. in 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015., 7299420, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015, Institute of Electrical and Electronics Engineers Inc., 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015, Cambridge, United States, 6/9/15. https://doi.org/10.1109/BSN.2015.7299420

Sano A, Phillips AJ, Yu AZ, McHill AW, Taylor S, Jaques N et al. Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones. In 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7299420. (2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015). doi: 10.1109/BSN.2015.7299420

Sano, Akane ; Phillips, Andrew J. ; Yu, Amy Z. et al. / Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones. 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015. Institute of Electrical and Electronics Engineers Inc., 2015. (2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015).

@inproceedings{751668e438f44e52b98c88228a1b9b1c,

title = "Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones",

abstract = "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%.",

keywords = "SMS, accelerometer, big five personality, machine learning, mental health, mobile phone, personality, phone call, skin conductance, sleep, smart phone, stress, wearable sensor",

author = "Akane Sano and Phillips, {Andrew J.} and Yu, {Amy Z.} and McHill, {Andrew W.} and Sara Taylor and Natasha Jaques and Czeisler, {Charles A.} and Klerman, {Elizabeth B.} and Picard, {Rosalind W.}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015 ; Conference date: 09-06-2015 Through 12-06-2015",

year = "2015",

month = oct,

day = "15",

doi = "10.1109/BSN.2015.7299420",

language = "English (US)",

series = "2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015",

}

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.

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 - SMS

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 - 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 -

Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this