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Explainable sleep quality evaluation model using machine learning approach

Title
Explainable sleep quality evaluation model using machine learning approach
Authors
Choi, Rock HyunKang, Won SeokSon, Chang Sik
DGIST Authors
Kang, Won Seok; Son, Chang Sik
Issue Date
2017-11-16
Citation
13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017, 542-546
Type
Conference
ISBN
9781509060641
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. © 2017 IEEE.
URI
http://hdl.handle.net/20.500.11750/6098
DOI
10.1109/MFI.2017.8170377
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
Files:
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Collection:
Convergence Research Center for Wellness2. Conference Papers


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