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A Novel Sleep Scoring Algorithm-Based Framework and Sleep Pattern Analysis Using Machine Learning Techniques

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  • Sabyasachi Chakraborty

    (Inje University, South Korea)

  • Satyabrata Aich

    (Inje University, South Korea)

  • Hee-Cheol Kim

    (Inje University, South Korea)

Abstract

Maintaining the suited amount of sleep is considered the prime component for maintaining a proper and adequate health condition. Often it has been observed that people having sleep inconsistency tend to jeopardize the health and appeal to many physiological and psychological disorders. To overcome such difficulties, it is often required to keep a requisite note of the duration and quality of sleep that one is having. This work defines an algorithm that can be utilized in smart wearables or mobile phones to perceive the duration of sleep and also to classify a particular instance as slept or awake on the basis of data fetched from the triaxial accelerometer. A comparative analysis was performed based on the results obtained from some previously developed algorithms, rule-based models, and machine learning models, and it was observed that the algorithm developed in the work outperformed the previously developed algorithms. Moreover, the algorithm developed in the work will very much define the scoring of sleep of an individual for maintaining a proper health balance.

Suggested Citation

  • Sabyasachi Chakraborty & Satyabrata Aich & Hee-Cheol Kim, 2021. "A Novel Sleep Scoring Algorithm-Based Framework and Sleep Pattern Analysis Using Machine Learning Techniques," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(3), pages 1-20, July.
  • Handle: RePEc:igg:jsda00:v:10:y:2021:i:3:p:1-20
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    References listed on IDEAS

    as
    1. Victor Stango & Jonathan Zinman, 2009. "Exponential Growth Bias and Household Finance," Journal of Finance, American Finance Association, vol. 64(6), pages 2807-2849, December.
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