오류신고
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오류신고
- 데이터 정보
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오류신고를 진행 하실 데이터 정보를 담은 표입니다. 제목(Main) FitBit Fitness Tracker Data 제목(Sub) 저자 Mobius; 제공처 국가연구데이터플랫폼 리포지터리 국가연구데이터플랫폼
- 접수 정보
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오류신고 접수 정보를 담은 표이며, 메일주소, 오류내용을 입력합니다. 아이디 오류신고 접수 정보를 담은 표이며, 메일주소, 오류내용을 입력합니다.오류 구분 - 개인정보 노출방지를 위해 개인정보 내용은 가급적 자제하여 주시기 바랍니다.
- 일방적인 욕설 및 부정적인 내용 작성시 원작자의 판단에 따라 신고자에게 피해가 발생할 수 있습니다. 깨끗하고 청렴한 서비스 문화를 위해 필요한 정보만 기재해주시면 감사하겠습니다.
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2021
해외
공개
CC-BY
English
FitBit Fitness Tracker Data
FitBit Fitness Tracker Data Mobius;
Pattern recognition with tracker data: : Improve Your Overall Health
Content
This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Individual reports can be parsed by export session ID (column A) or timestamp (column B). Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors / preferences.
Starter Kernel(s)
- Julen Aranguren: https://www.kaggle.com/julenaranguren/bellabeat-case-study
- Anastasiia Chebotina: https://www.kaggle.com/chebotinaa/bellabeat-case-study-with-r
Inspiration
- Human temporal routine behavioral analysis and pattern recognition
Acknowlegement
Furberg, Robert; Brinton, Julia; Keating, Michael ; Ortiz, Alexa
https://zenodo.org/record/53894#.YMoUpnVKiP9
Some readings
- How I analyzed the data from my FitBit to improve my overall health(https://www.freecodecamp.org/news/how-i-analyzed-the-data-from-my-fitbit-to-improve-my-overall-health-a2e36426d8f9/)
- How can data from fitness trackers be obtained and analyzed with a forensic approach?(https://conferences.computer.org/eurosp/pdfs/EuroSPW2020-7k9FlVRX4z43j4uE2SeXU0/859700a499/859700a499.pdf)
Content
This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Individual reports can be parsed by export session ID (column A) or timestamp (column B). Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors / preferences.
Starter Kernel(s)
- Julen Aranguren: https://www.kaggle.com/julenaranguren/bellabeat-case-study
- Anastasiia Chebotina: https://www.kaggle.com/chebotinaa/bellabeat-case-study-with-r
Inspiration
- Human temporal routine behavioral analysis and pattern recognition
Acknowlegement
Furberg, Robert; Brinton, Julia; Keating, Michael ; Ortiz, Alexa
https://zenodo.org/record/53894#.YMoUpnVKiP9
Some readings
- How I analyzed the data from my FitBit to improve my overall health(https://www.freecodecamp.org/news/how-i-analyzed-the-data-from-my-fitbit-to-improve-my-overall-health-a2e36426d8f9/)
- How can data from fitness trackers be obtained and analyzed with a forensic approach?(https://conferences.computer.org/eurosp/pdfs/EuroSPW2020-7k9FlVRX4z43j4uE2SeXU0/859700a499/859700a499.pdf)
- #business
- #KAGGLE
- #exercise
- #KAGGLE
데이터 생성 이력정보
특성 정보
- 주제분류 = 보건의료;문화/예술/체육
특성정보는 제공처로부터 수집된 데이터이며, DataON에서 제공하는 이외의 정보를 담고 있습니다.
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