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Models to Predict Sleeping Quality from Activities and Environment: Current Status, Challenges and Opportunities

Published: 21 August 2021 Publication History

Abstract

The development of remote/wearable sensors enables more research in the health care area. Based on these kinds of sensors, the information of human's active level, health parameters can be collected to predict one's health status. Sleeping quality is an important factor to make a person feel healthy. In this work, we summarize the current models to predict sleeping quality. Inputs of those models could be environmental factors, activities, or time-series data from wearable sensors. The characteristic of the input data may lead to the choice of prediction models. The domain of data that was used to forecast sleeping quality will be considered carefully in parallel with the prediction model. Challenges and future work for this research direction will be discussed in this paper.

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  • (2024)Predictive Sleep Disorder Modelling: Using Machine Learning with Lifestyle and Sleep Health Data2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)10.1109/ACCAI61061.2024.10602153(1-7)Online publication date: 9-May-2024
  • (2023)Can Sleep Quality Attributes be Predicted from Physical Activity in Everyday Settings?2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC40787.2023.10340421(1-5)Online publication date: 24-Jul-2023
  • (2023)AE-Sleep: An Adaptive Enhancement Sleep Quality System Utilizing Data Mining and Adaptive ModelSensing Technology10.1007/978-3-031-29871-4_5(31-47)Online publication date: 9-Apr-2023
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  1. Models to Predict Sleeping Quality from Activities and Environment: Current Status, Challenges and Opportunities

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    cover image ACM Conferences
    ICDAR '21: Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval
    August 2021
    72 pages
    ISBN:9781450385299
    DOI:10.1145/3463944
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 21 August 2021

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    Author Tags

    1. machine learning
    2. prediction model
    3. random forest
    4. sleeping efficiency

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    View all
    • (2024)Predictive Sleep Disorder Modelling: Using Machine Learning with Lifestyle and Sleep Health Data2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)10.1109/ACCAI61061.2024.10602153(1-7)Online publication date: 9-May-2024
    • (2023)Can Sleep Quality Attributes be Predicted from Physical Activity in Everyday Settings?2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC40787.2023.10340421(1-5)Online publication date: 24-Jul-2023
    • (2023)AE-Sleep: An Adaptive Enhancement Sleep Quality System Utilizing Data Mining and Adaptive ModelSensing Technology10.1007/978-3-031-29871-4_5(31-47)Online publication date: 9-Apr-2023
    • (2022)Monitoring and Improving Personalized Sleep Quality from Long-Term Lifelogs2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020829(4356-4364)Online publication date: 17-Dec-2022
    • (2021)A Personalized Adaptive Algorithm for Sleep Quality Prediction using Physiological and Environmental Sensing Data2021 8th NAFOSTED Conference on Information and Computer Science (NICS)10.1109/NICS54270.2021.9700990(113-119)Online publication date: 21-Dec-2021

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