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.

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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

• How can data from fitness trackers be obtained and analyzed with a forensic approach?

This dataset contains Axivity AX3 wrist-worn activity tracker data that were collected from 151 participants in 2014-2016 around the Oxfordshire area. Participants were asked to wear the device in daily living for a period of roughly 24 hours, amounting to a total of almost 4,000 hours. Vicon Autograph wearable cameras and Whitehall II sleep diaries were used to obtain the ground truth activities performed during the period (e.g. sitting watching TV, walking the dog, washing dishes, sleeping), resulting in more than 2,500 hours of labelled data. Accompanying code to analyse this data is available at https://github.com/activityMonitoring/capture24. The following papers describe the data collection protocol in full: i.) Gershuny J, Harms T, Doherty A, Thomas E, Milton K, Kelly P, Foster C (2020) Testing self-report time-use diaries against objective instruments in real time. Sociological Methodology doi: 10.1177/0081175019884591; ii.) Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. (2018) Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Scientific Reports. 8(1):7961. Regarding Data Protection, the Clinical Data Set will not include any direct subject identifiers. However, it is possible that the Data Set may contain certain information that could be used in combination with other information to identify a specific individual, such as a combination of activities specific to that individual ("Personal Data"). Accordingly, in the conduct of the Analysis, users will comply with all applicable laws and regulations relating to information privacy. Further, the user agrees to preserve the confidentiality of, and not attempt to identify, individuals in the Data Set.

Objectives: Activity trackers hold the promise to support people in managing their health through quantified measurements about their daily physical activities. Monitoring personal health with quantified activity tracker-generated data provides patients with an opportunity to self-manage their health. Many activity tracker user studies have been conducted within short time frames, however, which makes it difficult to discover the impact of the activity tracker’s novelty effect or the reasons for the device’s long-term use. This study explores the impact of novelty effect on activity tracker adoption and the motivation for sustained use beyond the novelty period.

Materials and Methods: This study uses a mixed-methods approach that combines both quantitative activity tracker log analysis and qualitative one-on-one interviews to develop a deeper behavioral understanding of 23 Fitbit device users who have used their trackers for at least two months (range of use = 69 - 1073 days).

Res...

LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit 

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema 

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys 

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{
  _id: 

COLLECTION ITEM:Data Authorization DocumentsCOLLECTION TITLE:2020_PiccininiEtAl_FitnessTracking_VideoARTICLE (when using this file, please, cite the following article):Filippo Piccinini, Giovanni Martinelli, Antonella Carbonaro, "Accuracy of mobile applications versus wearable devices in long-term step measurements". 2020.DESCRIPTION OF THE FILES IN THE COLLECTION:Authorization documents to process personal dataITEM TYPE (selected from those available):PDF manually signed.MAIN CONTACT FOR THESE FILES:Dr. Filippo Piccinini, PhD, IRST IRCCS Meldola Italy. Email: filippo.piccinini85@gmail.comMAIN AFFILIATIONS FOR THIS PROJECT:1) Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola (FC), Italy.2) University of Bologna, Italy.PROJECT DESCRIPTION:The Project focuses on challenges and opportunities today available to improve people’s well-being using IoT self-tracked Health Data. Recent statistics have shown that around 50% of people in developed countries make use of wearable devices to monitor fitness or physical activity (PA). Practically, people can constantly monitor their health status in an unobtrusive way at no cost and the great amount of patient-generated health data today available gives new opportunities to measure life parameters in real time and create a revolution in communication for professionals and patients. All the modern smartphones and fitness trackers are equipped with accelerometers that record accelerations in one or more planes. These data elements are processed into more meaningful variables, such as step counts; time spent in sedentary, light, moderate, or vigorous PA; and flights of stairs climbed. Besides discussing the current limits of the fitness tracking technologies, we supported the usage of wearable devices for mHealth and in general oncology-related analysis about cancer prevention, cancer treatment, and survivorship.PROJECT CATEGORY (selected from those available):Computer VisionPROJECT KEYWORDS (selected from those available):oncology; fitness training; wearable sensors; physical activities; statistical inference.LICENCE (selected from those available):GPL 3.0+

About the company Urška Sršen and Sando Mur founded Bellabeat, a high-tech company that manufactures health-focused smart products. Sršen used her background as an artist to develop beautifully designed technology that informs and inspires women around the world. Collecting data on activity, sleep, stress, and reproductive health has allowed Bellabeat to empower women with knowledge about their own health and habits. Since it was founded in 2013, Bellabeat has grown rapidly and quickly positioned itself as a tech-driven wellness company for women.

Characters ○ Urška Sršen: Bellabeat’s cofounder and Chief Creative Officer ○ Sando Mur: Mathematician and Bellabeat’s cofounder; key member of the Bellabeat executive team ○ Bellabeat marketing analytics team: A team of data analysts responsible for collecting, analyzing, and reporting data that helps guide Bellabeat’s marketing strategy. You joined this team six months ago and have been busy learning about Bellabeat’’s mission and business goals — as well as how you, as a junior data analyst, can help Bellabeat achieve them.

Content

I cleaned and analyzed the data via Google Sheets and imported it as an Excel file and created visualizations in Tableau Public. I found trends between Calories, Total Steps, Total Distance and Sedentary Minutes. All is explained in my word document case study.

Acknowledgements

I used data from "Fitbit Fitness Tracker Data" by the user Mobius.

Feel free to comment any mistakes made or things you would have done differently. This is my first case study and first time analyzing data. Any feedback will be gladly appreciated.

Context

Business Task: Review the data on how consumers are using non-Bellabeat smart devices to point out any trends. With the insights, analyze how those trends could be applied to one of Bellabeat’s products. Use the top usage trends for a marketing strategy to drive growth for Bellabeat.

Content

The data shows the smart device is used to track minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits.

Acknowledgements

Resources - Kaggle Fitbit Fitness Tracker Data by Mobius Furberg, Robert; Brinton, Julia; Keating, Michael ; Ortiz, Alexa https://zenodo.org/record/53894#.YMoUpnVKiP9 https://bellabeat.com/ https://www.omnicalculator.com/sports/met-minutes-per-week

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

A multi-modality, multi-activity, and multi-subject dataset of wearable biosignals.

Modalities: ECG, EMG, EDA, PPG, ACC, TEMP

Main Activities: Lift object, Greet people, Gesticulate while talking, Jumping, Walking, and Running

Cohort: 17 subjects (10 male, 7 female); median age: 24

Devices: 2x ScientISST Core + 1x Empatica E4

Body Locations: Chest, Abdomen, Left bicep, wrist and index finger

No filter has been applied to the signals, but the correct transfer functions were applied, so the data is given in relevant unis (mV, uS, g, ºC).

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There are two formats available:

a) LTBio's Biosignal files. Should be open like:

x = Biosignal.load(path)

LTBio Package: https://pypi.org/project/LongTermBiosignals/

Under the directory biosignal, the following tree structure is found: subject/x.biosignal, where subject is the subject’s code, and x is any of the following { acc_chest, acc_wrist, ecg, eda, emg, ppg, temp }. Each file includes the signals recorded from every sensor that acquires the modality after which the file is named, independently of the device.

Channels, activities and time intervals can be easily indexed with the index operator []: https://ltbio.readthedocs.io/en/latest/learn/basic/ltbio101.html

A sneak peak of the signals can also be quickly plotted with: x.preview.plot()

Any Biosignal can be easily converted to NumPy arrays or DataFrames, if needed.

b) CSV files. Can be open like:

x = pandas.read_csv(path)

Pandas Package: https://pypi.org/project/pandas/

These files can be found under the directory csv, named as subject.csv, where subject is the subject’s code. There is only one file per subject, containing their full session and all biosignal modalities. When read as tables, the time axis is in the first column, each sensor is in one of the middle columns, and the activity labels are in the last column. In each row are the samples of each sensor, if any, at each timestamp. At any given timestamp, if there is no sample for a sensor, it means the acquisition was interrupted for that sensor, which happens between activities, and sometimes for short periods during the running activity. Also in each row, on the last column, is one or more activity labels, if an activity was taking place at that timestamp. If there are multiple annotations, the labels are separated by commas (e.g 'run,sprint'). If there are no annotations, the column is empty for that timestamp.

In order to provide a tabular format with sensors with different sampling frequencies, the sensors with sampling frequency lower than 500 Hz were upsampled to 500 Hz. This way, the tables are regularly sampled, i.e., there is a row every 2 ms. If a sensor was not acquiring at a given timestamp, the corresponding cell with be empty. So, not only the segments with samples are regularly sampled, but the interruptions are also discretised. This means that if, after an interruption, a sensor starts acquiring at a non regular timestamp, the first sample will be written on the previous or the following timestamp, by half-up rounding. Naturally, this process cumulatively introduces lags in the table, some of which cancel out. Each individual lag is no longer than half the sampling period (1 ms), hence negligible. The cumulative lags are no longer than 200 ms for all subjects, which is also negligible. Nevertheless, only the LBio's Biosignal format preserves the exact original timestamps (10E-6 precision) of all samples and the original sampling frequencies.

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Both include annotations of the activities, however LTBio bio signal files have better time resolution and include clinical data and demographic data as well.