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aai530-group6
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sleep-score-fitbit

Tasks:
Time Series Forecasting
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Formats:
csv
Sub-tasks:
univariate-time-series-forecasting
Languages:
English
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Tags:
Health
Exercise
Health Conditions
Heart Conditions
Healthcare
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Fitbit Sleep Score Data

About the Dataset

Description

The Fitbit Sleep Score dataset, available on Kaggle, comprises detailed sleep data sourced from an individual's Fitbit device. It includes metrics such as overall sleep score, revitalization score, deep sleep duration, resting heart rate, and restlessness, each timestamped for in-depth analysis.

Data Fields

  • timestamp: The specific date and time the sleep data was recorded.
  • overall_score: An aggregate score up to 100, reflecting sleep duration, quality, and restoration.
  • revitalization_score: A score indicating the rejuvenating quality of the sleep.
  • deep_sleep_in_minutes: The amount of deep sleep achieved during the session, in minutes.
  • resting_heart_rate: The average resting heart rate recorded during sleep.
  • restlessness: A measure indicating the level of restlessness experienced during sleep.

Collection Methodology

This dataset was collected using a Fitbit Versa 4 device, capturing sleep data from a consenting individual. The data was then extracted from the Fitbit platform for analysis.

License

This dataset is made available under the MIT License, encouraging wide use for both academic and commercial purposes.

Updates

The dataset is expected to be updated annually to incorporate new data and insights.

Usage

This dataset is ideal for researchers and practitioners interested in sleep studies, health monitoring, and the development of health and wellness technologies. It provides a foundational resource for understanding and improving sleep quality and its impact on overall health.

Author

  • Maria Balos

Additional Notes

  • This dataset reflects the sleep patterns of a single individual and should be considered as a case study rather than a broad population sample.
  • Users are encouraged to respect privacy and ethical considerations when using this data.
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