Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones
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Picard_Recognizing academic.pdf
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286.13 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Phillips, Andrew J.
McHill, Andrew W.
Czeisler, Charles A.
Klerman, Elizabeth B.
Sano, Akane
Yu, Amy Z.
Taylor, Sara Ann
Jaques, Natasha Mary
Picard, Rosalind W.
Date Issued
October 2015
Journal
2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
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), June 2015, Cambridge, Massachusetts, Institute of Electrical and Electronics Engineers (IEEE), October 2015
Version
Author's final manuscript
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%.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/BSN.2015.7299420
