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 › peer-review

163 Citations (Scopus)

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 behavioural 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
Title of host publication	2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
Place of Publication	United States
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	6
ISBN (Electronic)	978-1-4673-7201-5
DOIs	https://doi.org/10.1109/BSN.2015.7299420
Publication status	Published - 19 Oct 2015
Externally published	Yes
Event	12th IEEE International Conference on Wearable and Implantable Body Sensor Networks - Cambridge, United States Duration: 9 Jun 2015 → 12 Jun 2015

Publication series

Name	IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN
Volume	2015
ISSN (Print)	2376-8886
ISSN (Electronic)	2376-8894

Conference

Conference	12th IEEE International Conference on Wearable and Implantable Body Sensor Networks
Abbreviated title	BSN 2015
Country/Territory	United States
City	Cambridge
Period	9/06/15 → 12/06/15

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BSN.2015.7299420Licence: In Copyright

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) Article 7299420 (IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN; Vol. 2015). Institute of Electrical and Electronics Engineers. https://doi.org/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). United States : Institute of Electrical and Electronics Engineers, 2015. (IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN).

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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 behavioural 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 = "accelerometer, big five personality, machine learning, mental health, mobile phone, personality, phone call, skin conductance, sleep, smart phone, SMS, 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}",

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

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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)., 7299420, IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN, vol. 2015, Institute of Electrical and Electronics Engineers, United States, 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, Cambridge, United States, 9/06/15. 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). United States: Institute of Electrical and Electronics Engineers, 2015. 7299420 (IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN; Vol. 2015).

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

TY - GEN

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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

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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

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M3 - Conference contribution

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ER -

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). United States: Institute of Electrical and Electronics Engineers. 2015. 7299420. (IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN). doi: 10.1109/BSN.2015.7299420

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

Abstract

Publication series

Conference

Keywords

UN SDGs

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