Explainable sleep quality evaluation model using machine learning approach

Rock Hyun Choi; Won Seok Kang; Chang Sik Son

doi:10.1109/MFI.2017.8170377

Explainable sleep quality evaluation model using machine learning approach

Rock Hyun Choi, Won Seok Kang, Chang Sik Son

Division of Intelligent Robot

Daegu Gyeongbuk Institute of Science and Technology

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

7 Scopus citations

Abstract

This research presents a scheme for explainable sleep quality evaluation utilizing the heart rate based sleep index. In the proposed model, the global covering rule induction of LERS (Learning from Examples based on Rough Sets) is used to generate rules associated with sleep quality status, such as 'Bad,' 'Normal,' and 'Good.' These rules are used to interpret the three sleep statuses. To show the applicability of the proposed scheme, we construct a sleep quality evaluation model based on sleep intraday time-series data collected from 280 factory and office workers with Fitbit fitness trackers. An evaluation of the proposed model was provided through statistical cross validation experiments.

Original language	English
Title of host publication	MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	542-546
Number of pages	5
ISBN (Electronic)	9781509060641
DOIs	https://doi.org/10.1109/MFI.2017.8170377
State	Published - 7 Dec 2017
Event	13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017 - Daegu, Korea, Republic of Duration: 16 Nov 2017 → 18 Nov 2017

Publication series

Name	IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Volume	2017-November

Conference

Conference	13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017
Country/Territory	Korea, Republic of
City	Daegu
Period	16/11/17 → 18/11/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Access to Document

10.1109/MFI.2017.8170377

Cite this

Choi, R. H., Kang, W. S., & Son, C. S. (2017). Explainable sleep quality evaluation model using machine learning approach. In MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (pp. 542-546). (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems; Vol. 2017-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MFI.2017.8170377

Choi, Rock Hyun ; Kang, Won Seok ; Son, Chang Sik. / Explainable sleep quality evaluation model using machine learning approach. MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 542-546 (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems).

@inproceedings{cf35a3b690e341e492ece3b4d19e58cd,

title = "Explainable sleep quality evaluation model using machine learning approach",

abstract = "This research presents a scheme for explainable sleep quality evaluation utilizing the heart rate based sleep index. In the proposed model, the global covering rule induction of LERS (Learning from Examples based on Rough Sets) is used to generate rules associated with sleep quality status, such as 'Bad,' 'Normal,' and 'Good.' These rules are used to interpret the three sleep statuses. To show the applicability of the proposed scheme, we construct a sleep quality evaluation model based on sleep intraday time-series data collected from 280 factory and office workers with Fitbit fitness trackers. An evaluation of the proposed model was provided through statistical cross validation experiments.",

author = "Choi, {Rock Hyun} and Kang, {Won Seok} and Son, {Chang Sik}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017 ; Conference date: 16-11-2017 Through 18-11-2017",

year = "2017",

month = dec,

day = "7",

doi = "10.1109/MFI.2017.8170377",

language = "English",

series = "IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems",

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

pages = "542--546",

booktitle = "MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems",

}

Choi, RH, Kang, WS & Son, CS 2017, Explainable sleep quality evaluation model using machine learning approach. in MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, vol. 2017-November, Institute of Electrical and Electronics Engineers Inc., pp. 542-546, 13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017, Daegu, Korea, Republic of, 16/11/17. https://doi.org/10.1109/MFI.2017.8170377

Explainable sleep quality evaluation model using machine learning approach. / Choi, Rock Hyun; Kang, Won Seok; Son, Chang Sik.
MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Institute of Electrical and Electronics Engineers Inc., 2017. p. 542-546 (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems; Vol. 2017-November).

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

TY - GEN

T1 - Explainable sleep quality evaluation model using machine learning approach

AU - Choi, Rock Hyun

AU - Kang, Won Seok

AU - Son, Chang Sik

PY - 2017/12/7

Y1 - 2017/12/7

N2 - This research presents a scheme for explainable sleep quality evaluation utilizing the heart rate based sleep index. In the proposed model, the global covering rule induction of LERS (Learning from Examples based on Rough Sets) is used to generate rules associated with sleep quality status, such as 'Bad,' 'Normal,' and 'Good.' These rules are used to interpret the three sleep statuses. To show the applicability of the proposed scheme, we construct a sleep quality evaluation model based on sleep intraday time-series data collected from 280 factory and office workers with Fitbit fitness trackers. An evaluation of the proposed model was provided through statistical cross validation experiments.

AB - This research presents a scheme for explainable sleep quality evaluation utilizing the heart rate based sleep index. In the proposed model, the global covering rule induction of LERS (Learning from Examples based on Rough Sets) is used to generate rules associated with sleep quality status, such as 'Bad,' 'Normal,' and 'Good.' These rules are used to interpret the three sleep statuses. To show the applicability of the proposed scheme, we construct a sleep quality evaluation model based on sleep intraday time-series data collected from 280 factory and office workers with Fitbit fitness trackers. An evaluation of the proposed model was provided through statistical cross validation experiments.

UR - http://www.scopus.com/inward/record.url?scp=85042364216&partnerID=8YFLogxK

U2 - 10.1109/MFI.2017.8170377

DO - 10.1109/MFI.2017.8170377

M3 - Conference contribution

AN - SCOPUS:85042364216

T3 - IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

SP - 542

EP - 546

BT - MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017

Y2 - 16 November 2017 through 18 November 2017

ER -

Choi RH, Kang WS, Son CS. Explainable sleep quality evaluation model using machine learning approach. In MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Institute of Electrical and Electronics Engineers Inc. 2017. p. 542-546. (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems). doi: 10.1109/MFI.2017.8170377

Explainable sleep quality evaluation model using machine learning approach

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this