Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: | http://hdl.handle.net/1721.1/110680 https://orcid.org/0000-0003-4484-8946 https://orcid.org/0000-0003-4133-9230 https://orcid.org/0000-0002-8413-9469 https://orcid.org/0000-0002-5661-0022 |
| _version_ | 1811080738098905088 |
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| author | 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. |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory 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. |
| author_sort | Phillips, Andrew J. |
| collection | MIT |
| description | 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%. |
| first_indexed | 2024-09-23T11:35:59Z |
| format | Article |
| id | mit-1721.1/110680 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:35:59Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1106802022-10-01T04:43:37Z Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones 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. Massachusetts Institute of Technology. Media Laboratory Sano, Akane Yu, Amy Z. Taylor, Sara Ann Jaques, Natasha Mary Picard, Rosalind W. 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%. 2017-07-12T17:19:42Z 2017-07-12T17:19:42Z 2015-10 2015-06 Article http://purl.org/eprint/type/ConferencePaper 978-1-4673-7201-5 978-1-4673-7202-2 http://hdl.handle.net/1721.1/110680 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 https://orcid.org/0000-0003-4484-8946 https://orcid.org/0000-0003-4133-9230 https://orcid.org/0000-0002-8413-9469 https://orcid.org/0000-0002-5661-0022 en_US http://dx.doi.org/10.1109/BSN.2015.7299420 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | 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. Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones |
| title | Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones |
| title_full | Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones |
| title_fullStr | Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones |
| title_full_unstemmed | Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones |
| title_short | Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones |
| title_sort | recognizing academic performance sleep quality stress level and mental health using personality traits wearable sensors and mobile phones |
| url | http://hdl.handle.net/1721.1/110680 https://orcid.org/0000-0003-4484-8946 https://orcid.org/0000-0003-4133-9230 https://orcid.org/0000-0002-8413-9469 https://orcid.org/0000-0002-5661-0022 |
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