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Explainable sleep quality evaluation model using machine learning approach
- Explainable sleep quality evaluation model using machine learning approach
- Choi, Rock Hyun; Kang, Won Seok; Son, Chang Sik
- DGIST Authors
- Kang, Won Seok; Son, Chang Sik
- Issue Date
- 13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017, 542-546
- 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.
- Institute of Electrical and Electronics Engineers Inc.
- Related Researcher
Data Mining & Machine Learning for Text & Multimedia, Brain-Sense-ICTConvergence Computing, Computational Olfaction Measurement, Simulation&Modeling
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- Convergence Research Center for Wellness2. Conference Papers
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