This dataset contains a list of 423 consumer-based wrist-worn activity trackers and smart watches, capable of collecting and estimating physical activity levels in individuals, using accelerometer and other sensors. Data were collected by automatic and manual searches through six online and offline databases, as well as manual collecting of data from company web sites. Data were collected in 2017, and contains all identified devices released between 2011 (earliest identified device) and July 2017. For each device, 12 attributes are included. See list in the ReadMe file.

Wearable Activity Tracker Data

SQLite database of wearable activity tracker users (N=88) data collected for 4 months.

The data has been collected between May 15th 2020 and September 15th 2020 in Switzerland, and is part of the data originally used in this study.

The data was collected with a Fibit Inspire HR.

• Age of participants: min. age: 18 y.o., max age: 31 y.o., mean age: 21.1 y.o., std age: 2.2 y.o.
• Gender of participants: women: 67%, men: 33%

For each of the 88 users, we collected:

• step count for every minute
• heart rate for every minute
• resting heart rate
• sleep data
  • fall asleep time
  • duration
  • sleep quality
  • restless times
  • restless duration
• all automatically detected activities
  • type (e.g., walking, swimming)
  • duration
  • time
• gender (as declared in the Fitbit profile)

Explore Fitness, Health, and Wellness Through Comprehensive Tracker Data

This dataset contains 29 merged files covering minute-level, hourly, and daily tracking across multiple health and wellness metrics. The data is split into two distinct time periods:

• Export 1: March 12, 2016 - April 11, 2016
• Export 2: April 12, 2016 - May 12, 2016

These exports provide detailed insights into user behavior patterns using Fitbit devices, allowing for robust analyses in health and fitness trends.
Dataset Features: 1. Daily Activity:
- Aggregated metrics for steps, calories, and intensity.
- Files: dailyActivity_merged.csv, dailyCalories_merged.csv, dailyIntensities_merged.csv, dailySteps_merged.csv.

1. Hourly Data:

   • Hourly breakdowns of activity and calorie burn.
   • Files: hourlyCalories_merged.csv, hourlyIntensities_merged.csv, hourlySteps_merged.csv.

2. Minute-Level Data:

   • High-resolution data in narrow and wide formats for calories, steps, intensity, and METs.
   • Files:
     • Narrow: minuteCaloriesNarrow_merged.csv, minuteIntensitiesNarrow_merged.csv, minuteStepsNarrow_merged.csv, minuteMETsNarrow_merged.csv.
     • Wide: minuteCaloriesWide_merged.csv, minuteIntensitiesWide_merged.csv, minuteStepsWide_merged.csv.

3. Heart Rate:

   • Second-by-second heart rate data for precise analysis.
   • File: heartrate_seconds_merged.csv.

4. Sleep Data:

   • Insights into sleep quality, duration, and patterns.
   • Files: minuteSleep_merged.csv, sleepDay_merged.csv.

5. Weight Logs:

   • Tracking user weight and trends over time.
   • File: weightLogInfo_merged.csv.

Who can use it:

• Health Behavior Analysis: Study routines, anomalies, and behavioral trends in activity, sleep, and heart rate.
• Machine Learning Applications: Develop predictive models for fatigue, health risks, or fitness improvements.
• Wearable Technology Research: Evaluate user engagement with fitness trackers and related behavioral insights.
• Personalized Wellness Studies: Correlate heart rate, activity levels, and sleep to derive personalized health strategies.

Usage:

1. Fitness and Wellness Trends: Uncover patterns in activity, sleep, and heart rate data.
2. Temporal Analysis: Study how routines and behaviors change over time.
3. Predictive Analytics: Build models to predict fatigue or health risks using granular data.
4. Wearable Insights: Enhance the understanding of Fitbit devices and their impact on user health.

License

Free for public use.

📌 Acknowledgment

This dataset was collected and shared by:
Robert Furberg, Julia Brinton, Michael Keating, and Alexa Ortiz

Original Source:

• Zenodo: Fitbit Data Archive

Contributors to related analyses:
- Julen Aranguren
- Anastasiia Chebotina

Data were collected from 113 participants, who shared their physical activity (PA) data using privately owned smart watches and activity trackers from Garmin and Fitbit. This data set consists of two data files: "data.csv" and "data raw.csv": The first file ("data.csv") contains daily averages for steps, total energy expenditure (TEE), activity energy expenditure (AEE), moderate-to-vigorous physical activity (MVPA), light PA (LPA), moderate PA (MPA), vigorous PA (VPA), and sedentary time, grouped by month. In addition, daily averages for the whole year of 2019 and 2020 are included. Finally, separate variables for the first and second half of March 2020 (pre- and post COVID-19 lockdown in Norway) are included. The second file ("data raw.csv") contains raw daily values for steps, TEE, AEE, MVPA, LPA, MPA, VPA, sedentary time, and non-wear time.

This data used for this analysis contains personal fitness tracker from thirty fitbit users. Approximately thirty eligible Fitbit users consented to the submission of personal tracker data, including 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.

There are several limitations to this data set that may skew or cause our analysis to not be completely conclusive. These limitations include the following:

1. Data is from 2016 and consumer activity may have changed since then
2. The sample size for our analysis is very small. Making assumptions about the exercise habits of all users based upon a sample of ~30 participants may not be accurate.
3. External factors cannot be accounted for accurately. The data set does not provide information about the age, sex, career, lifestyle, etc. of the participants.

About this Data: This dataset was generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors/preferences. Per the Amazon Mechanical Turk Website: "Amazon Mechanical Turk is a forum where Requesters post work as Human Intelligence Tasks (HITs). Workers complete HITs in exchange for a reward. You write, test, and publish your HIT using the Mechanical Turk developer sandbox, Amazon Mechanical Turk APIs, and AWS SDKs."

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?

FitBit Fitness Tracker Data (CC0: Public Domain, dataset made available through Mobius): This Kaggle data set contains personal fitness tracker from thirty fitbit users. Thirty eligible Fitbit users consented to the submission of personal tracker data, including 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.

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.

Introduction: Consumer-wearable activity trackers are small electronic devices engineered to monitor and record fitness and health-related measures. The purpose of this systematic review is to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure. Method: We extracted information about commercial wearable devices (e.g., price, size, battery life, sensors, measurements, algorithms) using an Internet search conducted from November 2016- January 2017. From this search we identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only included articles published in the English language up to May 2019. Studies were excluded if they did not identify the device used and if they did not examine the validity and/or reliability of a device. Studies including the general population and all special populations were included. We operationalized validity as criterion (as compared to other measures) and construct (degree to which device is measuring what it purports) validity. Reliability measures focused on intradevice and interdevice reliability. Results: We included 158 publications examining 9 different commercial wearable device brands. Fitbit was by far the most studied brand. In lab-based settings Fitbit, Apple, and Samsung appeared to measure steps accurately. Heart rate was more variable with Apple Watch, Garmin was the most accurate and Fitbit tended towards underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand. Conclusion: Activity trackers are still an emerging market and the devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.

Analysis of ‘Bellabeaat fitness tracker device data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/blessingalabie/bellabeaat-fitness-tracker-device-data on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

The data contained in this dataset was collected from fitness tracker devices made for women. This dataset contains information collated from bellabeat (fitbit) fitness tracker devices.

Content

The fitness tracker collects the following data;

• Calories burnt per hour, and per day
• Distance covered per day
• Distance covered (at different activity intensities) per day
• Total steps per hour, per day
• Sleep time per day

Acknowledgements

My acknowledgement goes to the Google Data Analytics team for sharing this dataset, enabling starters like myself practice and show-off our data analytic skills

Inspiration

The dataset was created for analysis, to enable the Fitbit team gain insights into how their customers currently use the device and enable them create effective marketing strategy to further promote their product.

--- Original source retains full ownership of the source dataset ---

This dataset contains wrist-worn activity tracker data collected from 151 participants for a period of roughly 24hs in natural settings, annotated using wearable cameras and sleep diaries.

Context

This data was collected to complete the Google data analytics certification capstone project.

Content

This data from approximately 33 FitBit users was collected. The data includes users' activity levels, calories burned, sleep data, and more. Each dataset contains users' Id's and a timestamp.

Inspiration

This is the first step in my data analytics journey!

Dataset

This dataset was created by Ryan Mutch

Contents

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: 

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...

This dataset contains Axivity AX3 activity tracker (accelerometer) data collected from 39 participants in 2019-2020 around the Oxfordshire area. Calibrated and resampled traxial acceleration data is included, captured during unscripted, free living in healthy adult volunteers (aged 18 and above) with no lower limb injury within the previous 6 months and who were able to walk without an assistive device. Participants wore four triaxial accelerometers concurrently (AX3, Axivity, Newcastle, UK), two placed side-by-side on the dominant wrist and two clipped to the dominant-side hip at the midsagittal plane. Accelerometers were synchronised using the Open Movement GUI software (v.1.0.0.42), with one recording at 100 Hz and the other at 25 Hz at each body location. Foot-facing video was captured using an action camera (Action Camera CT9500, Crosstour, Shenzhen, China) mounted at the participant’s beltline. From the synchronised camera data, a step is annotated in each CSV file by a single "1" at the approximate time of heel strike.

A full data description is available in README.txt upon download.

Dataset

This dataset was created by Nneka Ekwemuka

Released under CC0: Public Domain

Contents

Analyst: Alexandra Loop Date: 12/02/2024

Business Task:

Question to be Answered : - What are trends in non-Bellabeat smart device usage? - What do these trends suggest for Bellabeat customers? - How could these trends help influence Bellabeat marketing strategy?

Description of Data Sources:

Data Set to be studied: FitBit Fitness Tracker Data: Pattern Recognition with tracker data: Improve Your Overall Health

Data privacy: Data was sourced from a public dataset available on Kaggle. Information has been anonymized prior to being posted online.

Bias: Due to the degree of anonymity in this study, the only demographic data available in this study is weight, and other cultural differences or lifestyle requirements cannot be accounted for. The sample size is quite small. The time period of the study is only a month so the observer effect could conceivably still be influencing the sample groups. We also have no information on the weather in the region studied. April and May are very variable months in terms of accessible outdoor activities.

Process:

Cleaning Process: After going through the data to find duplicates, whitespace, and nulls, I have determined that this set of data has been well-cleaned and already aggregated into several reasonably sized spreadsheets.

Trim: No issues found

Consistent length ID: No issues found

Irrelevant columns: In WLI_M the fat column is not consistently filled in so it is not productive to use it in analysis Sedentary_active_distance was mostly filled with nulls and could confuse the data I have removed the columns

Irrelevant Rows: 77 rows in daily_Activity_merged had 0s across the board. As there is little chance that someone would take zero steps I decided to interpret these days as ones where people did not put on the fitbit. As such they are irrelevant rows. Removed 77 columns. 85 rows in daily_intensities_merged registered 0 minutes of sedentary activity, which I do not believe to be possible. Row 241 logged 2 minutes of sedentary activity. I have determined it to be unusable. Row 322 likewise does not add up to a day’s minutes and has been deleted. Removed 85 columns 7 rows had 1440 sedentary minutes, which I have determined to be time on but not used. Implication of the presence noted.

Scientifically debunked information: BMI as a measurement has been determined to be problematic on many lines, it misrepresents non-white people who have different healthy body types, does not account for muscle mass or scoliosis, has been known to change definitions in accordance with business interests rather than health data, and was never meant to be used as a measure of individual health. I have removed the BMI column from the Weight Log Info chart.

Cleaning Process 1: I have elected to see what can be found in the data as it was organized by the providers first.
Cleaning Process 2: I calculated and removed rows where the participants did not put on the fitbit. These rows were removed, and the implications of their presence have been noted. Found Averages, Minimum, and Maximum Values of Steps, distance, types of active minutes, and calories. Found the sum of all kinds of minutes documented to check for inconsistencies. Found the difference between total minutes and a full 1440 minutes. I tried to make a pie chart to convey the average minutes of activity, and so created a duplicate dataset to trim down and remove misleading data caused by different inputs.

Analysis:

Observations: On average, the participants do not seem interested in moderate physical activity as it was the category with the fewest number of active minutes. Perhaps advertise the effectiveness of low impact workouts. Very few participants volunteered their weights, but none of them lost weight. The person with the highest weight volunteered it only once near the beginning. Given evidence from the Health At Every Size movement, we cannot deny the possibility that having to be weight conscious could have had negative effects on this individual. I would suggest that weight would be a counterproductive focus for our marketing campaign as it would make heavier people less likely to want to participate, and any claims of weight loss would be statistically unfounded, and open us up to false advertising lawsuits. Fully half of the participants had days where they did not put on their fitbit at all during the day. For a total number of 77-84 lost days of data, meaning that on average participants who did not wear their fitbit daily lost 5 days of data, though of course some lost significantly more. I would suggest focusing on creating a biometric tracker that is comfortable and rarely needs to be charged so that people will gain more reliable resources from it. 400 full days of data are recorded, meaning that the participants did not take the device off to sleep, shower, or swim. 280 more have 16...

The dataset is provided in the form of an excel files with 5 tabs. The first three excel tabs constitute demonstration data on the set up of consumer wearable device for exposure and health monitoring in population studies while the two last excel tabs include the full dataset with actual data collected using the consumer wearable devices in Cyprus and Greece respectively during the Spring of 2020. The data from the last two tabs were used to assess the compliance of asthmatic schoolchildren (n=108) from both countries to public health intervention levels in response to COVID-19 pandemic (lockdown and social distancing measures), using wearable sensors to continuously track personal location and physical activity. Asthmatic children were recruited from primary schools in Cyprus and Greece (Heraklion district, Crete) and were enrolled in the LIFE-MEDEA public health intervention project (Clinical.Trials.gov Identifier: NCT03503812). The LIFE-MEDEA project aimed to evaluate the efficacy of behavioral recommendations to reduce exposure to particulate matter during desert dust storm (DDS) events and thus mitigate disease-specific adverse health effects in vulnerable groups of patients. However, during the COVID-19 pandemic, the collected data were analysed using a mixed effect model adjusted for confounders to estimate the changes in 'fraction time spent at home' and 'total steps/day' during the enforcement of gradually more stringent lockdown measures. Results of this analysis were first presented in the manuscript titled “Use of wearable sensors to assess compliance of asthmatic children in response to lockdown measures for the COVID-19 epidemic” published by Scientific Reports (https://doi.org/10.1038/s41598-021-85358-4). The dataset from LIFE-MEDEA participants (asthmatic children) from Cyprus and Greece, include variables: Study ID, gender, age, study year, ambient temperature, ambient humidity, recording day, percentage of time staying at home, steps per day, callendar day, calendar week, date, lockdown status (phase 1, 2, or 3) due to COVID-19 pandemic, and if the date was during the weekend (binary variable). All data were collected following approvals from relevant authorities at both Cyprus and Greece, according to national legislation. In Cyprus, approvals have been obtained from the Cyprus National Bioethics Committee (EEBK EΠ 2017.01.141), by the Data Protection Commissioner (No. 3.28.223) and Ministry of Education (No 7.15.01.23.5). In Greece, approvals have been obtained from the Scientific Committee (25/04/2018, No: 1748) and the Governing Board of the University General Hospital of Heraklion (25/22/08/2018).

Overall, wearable sensors, often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessment in clinical studies. Nevertheless, the real-life application of these technologies in studies involving many participants for a significant observation period may be hindered by several practical challenges. Using a small subset of the LIFE-MEDEA dataset, in the first excel tab of dataset, we provide demonstration data from a small subset of asthmatic children (n=17) that participated in the LIFE MEDEA study that were equipped with a smartwatch for the assessment of physical activity (heart rate, pedometer, accelerometer) and location (exposure to indoor or outdoor microenvironment using GPS signal). Participants were required to wear the smartwatch, equipped with a data collection application, daily, and data were transmitted via a wireless network to a centrally administered data collection platform. The main technical challenges identified ranged from restricting access to standard smartwatch features such as gaming, internet browser, camera, and audio recording applications, to technical challenges such as loss of GPS signal, especially in indoor environments, and internal smartwatch settings interfering with the data collection application. The dataset includes information on the percentage of time with collected data before and after the implementation of a protocol that relied on setting up the smartwatch device using publicly available Application Lockers and Device Automation applications to address most of these challenges. In addition, the dataset includes example single-day observations that demonstrate how the inclusion of a Wi-Fi received signal strength indicator, significantly improved indoor localization and largely minimised GPS signal misclassification (excel tab 2). Finally excel tab 3, shows the tasks Overall, the implementation of these protocols during the roll-out of the LIFE MEDEA study in the spring of 2020 led to significantly improved results in terms of data completeness and data quality. The protocol and the representative results have been submitted for publication to the Journal of Visualised experiments (submission: JoVE63275). The Variables included in the first three excel tabs were the following: Participant ID (Unique serial number for patient participating in the study), % Time Before (Percentage of time with data before protocol implementation), % Time After (Percentage of time with data after protocol implementation), Timestamp (Date and time of event occurrence), Indoor/Outdoor (Categorical- Classification of GPS signals to Indoor and Outdoor and null(missing value) based on distance from participant home), Filling algorithm (Imputation algorithm), SSID (Wireless network name connected to the smartwatch), Wi-Fi Signal Strength (Connection strength via Wi-Fi between smartwatch and home’s wireless network. (0 maximum strength), IMEI (International mobile equipment identity. Device serial number), GPS_LAT (Latitude), GPS_LONG (Longitude), Accuracy of GPS coordinates (Accuracy in meters of GPS coordinates), Timestamp of GPS coordinates (Obtained GPS coordinates Date and time), Battery Percentage (Battery life), Charger (Connected to the charger status).

Important notes on data collection methodology: Global positioning system (GPS) and physical activity data were recorded using LEMFO-LM25 smartwatch device which was equipped with the embrace™ data collection application. The smartwatch worked as a stand-alone device that was able to transmit data across 5-minute intervals to a cloud-based database via Wi-Fi data transfer. The software was able to synchronize the data collected from the different sensors, so the data are transferred to the cloud with the same timestamp. Data synchronization with the cloud-based database is performed automatically when the smartwatch contacts the Wi-Fi network inside the participants’ homes. According to the study aims, GPS coordinates were used to estimate the fraction of time spent in or out of the participants' residences. The time spent outside was defined as the duration of time with a GPS signal outside a 100-meter radius around the participant’s residence, to account for the signal accuracy in commercially available GPS receivers. Additionally, to address the limitation that signal accuracy in urban and especially indoor environments is diminished, 5-minute intervals with missing GPS signals were classified as either “indoor classification” or “outdoor classification” based on the most recent available GPS recording. The implementation of this GPS data filling algorithm allowed replacing the missing 5-minute intervals with estimated values. Via the described protocol, and through the use of a Device Automation application, information on WiFi connectivity, WiFi signal strength, battery capacity, and whether the device was charging or not was also made available. Data on these additional variables were not automatically synchronised with the cloud-based database but had to be manually downloaded from each smartwatch via Bluetooth after the end of the study period.

This dataset describes the data collected from physical activity and indoor location systems.