Content

This dataset represents sequential poses that can be used to distinguish 5 physical exercises. The exercises are Push-up, Pull-up, Sit-up, Jumping Jack and Squat. The dataset consists of 33 landmarks that represents several important body parts' positions. Using these landmarks, the angles and the distances between several landmarks are calculated and included in the dataset. The sequence of the poses is provided by preserving the frame order in every record.

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Collection Process

About 500 videos of people doing the exercises have been used in order to collect this data. The videos are from Countix Dataset that contain the YouTube links of several human activity videos. Using a simple Python script, the videos of 5 different physical exercises are downloaded. All the frames of the videos are extracted, processed and included in the dataset.

Processing Data

For every frame, MediaPipe framework is used for applying pose estimation, which detects the human skeleton of the person in the frame. The landmark model in MediaPipe Pose predicts the location of 33 pose landmarks (see figure below). Visit Mediapipe Pose Classification page for more details. Using these landmarks, the angles and the distances between several landmarks are calculated.

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This Synthetic Gym Members Exercise Dataset is created for educational and research purposes in fitness, public health, and data science. It provides detailed demographic, physiological, and workout-related information about gym members, enabling analysis of exercise patterns, health metrics, and fitness progress. The dataset can be utilized for building predictive models and exploring personalized workout and fitness management strategies.

Dataset Features

• Age: Age of the gym member in years.
• Gender: Biological sex of the gym member (Male/Female).
• Weight (kg): Weight of the individual in kilograms.
• Height (m): Height of the individual in meters.
• Max_BPM: Maximum heartbeats per minute during exercise.
• Avg_BPM: Average heartbeats per minute during exercise.
• Resting_BPM: Resting heartbeats per minute.
• Session_Duration (hours): Duration of the exercise session in hours.
• Calories_Burned: Total calories burned during the workout session.
• Workout_Type: Type of workout performed (e.g., HIIT, Yoga, Cardio).
• Fat_Percentage: Body fat percentage of the individual.
• Water_Intake (liters): Water intake during the workout session in liters.
• Workout_Frequency (days/week): Number of workout days per week.
• Experience_Level: Experience level of the gym member (1 = Beginner, 2 = Intermediate, 3 = Advanced).
• BMI: Body Mass Index, calculated as weight (kg) / (height (m))².

Distribution

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Usage

This dataset is suited for the following applications:

• Health and Fitness Insights: Analyze relationships between BMI, workout types, and health metrics like fat percentage or heart rate.
• Personalized Exercise Plans: Develop algorithms to recommend tailored workout routines based on individual fitness levels and goals.
• Calorie Burn Prediction: Build predictive models to estimate calories burned during workout sessions based on key features.
• Public Health Research: Study exercise trends and their impact on health outcomes.
• Fitness Tracking: Use data to monitor individual or group fitness progress over time. ### Coverage This synthetic dataset is anonymized and adheres to data privacy standards. It is designed for research and learning purposes, with diverse cases representing various fitness levels, workout types, and health metrics.

License

CC0 (Public Domain)

Who Can Use It

• Data Science Practitioners: For practicing data preprocessing, regression, and classification tasks related to fitness and health.
• Fitness Professionals and Researchers: To explore trends and patterns in gym members' workout habits and health outcomes.
• Public Health Analysts: To design effective strategies promoting physical activity and healthy lifestyles.
• Policy Makers and Regulators: For data-driven decision-making to promote fitness and public health initiatives.

1) Data Introduction • The Gym Members Exercise Dataset is a dataset built to systematically collect gym members' exercise routines, body information, exercise habits, and fitness indicators to analyze individual exercise patterns and health conditions.

2) Data Utilization (1) Gym Members Exercise Dataset has characteristics that: • This dataset contains various body and exercise related numerical and categorical variables such as age, gender, weight, height, body fat percentage, BMI, exercise type (e.g., aerobic, muscular, yoga, HIIT), exercise frequency, session time, heart rate (maximum, average, rest), calorie consumption, water intake, and experience level. (2) Gym Members Exercise Dataset can be used to: • Exercise effect analysis and customized fitness strategy: Various variables such as exercise type, frequency, session time and heart rate, calorie burn, body fat percentage, etc. can be analyzed and used to establish customized exercise plans for each member and optimize exercise effectiveness. • Healthcare and Member Characteristics Based Marketing: Based on demographics and exercise habit data such as age, gender, and experience level, it can be used to develop healthcare programs, segment members, and establish targeted marketing strategies.

This dataset is from the 2013 California Dietary Practices Survey of Adults. This survey has been discontinued. Adults were asked a series of eight questions about their physical activity practices in the last month. These questions were borrowed from the Behavior Risk Factor Surveillance System. Data displayed in this table represent California adults who met the aerobic recommendation for physical activity, as defined by the 2008 U.S. Department of Health and Human Services Physical Activity Guidelines for Americans and Objectives 2.1 and 2.2 of Healthy People 2020.

The California Dietary Practices Surveys (CDPS) (now discontinued) was the most extensive dietary and physical activity assessment of adults 18 years and older in the state of California. CDPS was designed in 1989 and was administered biennially in odd years up through 2013. The CDPS was designed to monitor dietary trends, especially fruit and vegetable consumption, among California adults for evaluating their progress toward meeting the 2010 Dietary Guidelines for Americans and the Healthy People 2020 Objectives. For the data in this table, adults were asked a series of eight questions about their physical activity practices in the last month. Questions included: 1) During the past month, other than your regular job, did you participate in any physical activities or exercise such as running, calisthenics, golf, gardening or walking for exercise? 2) What type of physical activity or exercise did you spend the most time doing during the past month? 3) How many times per week or per month did you take part n this activity during the past month? 4) And when you took part in this activity, for how many minutes or hours did you usually keep at it? 5) During the past month, how many times per week or per month did you do physical activities or exercises to strengthen your muscles? Questions 2, 3, and 4 were repeated to collect a second activity. Data were collected using a list of participating CalFresh households and random digit dial, approximately 1,400-1,500 adults (ages 18 and over) were interviewed via phone survey between the months of June and October. Demographic data included gender, age, ethnicity, education level, income, physical activity level, overweight status, and food stamp eligibility status. Data were oversampled for low-income adults to provide greater sensitivity for analyzing trends among our target population.

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: 

A meticulously compiled dataset providing deep insights into the global fitness industry in 2025. This dataset covers high-demand topics such as the exponential growth of fitness clubs, emerging trends in boutique fitness studios, skyrocketing online fitness training statistics, the flourishing fitness equipment market, and changing consumer behavior and expenditure patterns in the fitness sector.

This dataset was created by myself. This dataset contains videos of people doing workouts. The name of the existing workout corresponds to the name of the folder listed.

Video format: .mp4 Some of the videos are muted

What is the videos resolution? The resolution of this video varies greatly, but I'm trying to find the best possible resolution so that you can lower the resolution according to what you will use later.

How about the duration of the videos? It also varies, but there is at least 1 rep on each video

What are the data sources? Mostly sourced from YouTube, but I also create some of it by myself with my friends

Need the extracted frame of each video? Try check my other dataset for the images of workout/exercise here

ABSTRACT Introduction According to the 2015 National Physical Health Monitoring Report, most of the national physical health indicators have begun to rebound, but some people’s physical health is still declining. Object The thesis studies the problems existing in people’s physical exercise and guides the development of these people’s habits. Methods Our mathematical statistics and other research methods investigate the current situation of people’s physical exercise habits, and explore the factors that restrict habits from the factors that affect the formation of sports and fitness concepts. Result The proportion of people developing physical exercise habits is low. People invest less time and energy in physical exercise. Conclusion The less time and energy that people invest in physical exercise is the main reason that affects their belief in exercise and fitness and physical exercise habits. Level of evidence II; Therapeutic studies - investigation of treatment results.

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...

Statistics from the National Health Survey (2017-18) indicate that 54% of females (35-44 y.o) fail to reach recommended physical activity levels which is unchanged from 2011-2012 figures. Additionally, a reported 80% of women currently have insufficient resistance exercise training levels to achieve optimal health benefits for chronic disease prevention. While the exact reason is not known, fitness professionals may play a role. The role of fitness professionals in delivering physical activity for healthy agendas has been examined and demonstrated that fitness professionals can both educate and support individuals to increase physical activity levels. With growing public awareness in the fitness industry, Personal Trainers and other health professionals require skills to promote engagement and adherence to physical activity, particularly resistance exercise training. Whilst each profession has their own scope of practice, in order to achieve a positive outcome, which to date has been limited/stagnant, some commonality between, and within scope of practices, may need to be achieved. Utilising the large body of research existing around elements to guide resistance training promotion and engagement, my project is aiming to successfully ascertain interest in provision of education/resources/advice to link research to the 'real world'. A second objective is to then develop a tool focussed on eliciting preferences and behaviours towards physical activity which could be useful to a broader sector of the community. Specifically, such a tool could be adopted by health professionals such as Exercise Physiologists, Exercise Scientists and Personal Trainers, to use within their scope of practice to better engage middle aged women in resistance exercise to sufficient levels for chronic disease prevention.

This dataset relates to a manuscript on associations between sports measures and limitations in IADL among older persons. These data are part of the Dutch ELANE (Elderly And their Neighbourhood) study. Within ELANE, associations were studied between area characteristics and physical activity, independent living, and quality of life among community-dwelling people aged 65 years and older, living in Spijkenisse, a middle-sized town in the Rotterdam area.

The related article is (see also relations): Etman A, Pierik FH, Kamphuis CB, Burdorf A, van Lenthe FJ. The role of high-intensity physical exercsie in the prevention of disability among community-dwelling older people. BMC Geriatrics, 2016 Nov 9;16(1):183.

The ISAR-HP questionnaire is not available in the dataset due to copyright restrictions. A link to the online version can be found under 'Relations'.

2016-11-15
The related article is now included in the dataset:
Etman A, Pierik FH, Kamphuis CB, Burdorf A, van Lenthe FJ. The role of high-intensity physical exercsie in the prevention of disability among community-dwelling older people. BMC Geriatrics, 2016 Nov 9;16(1):183.

UI-PRMD is a data set of movements related to common exercises performed by patients in physical therapy and rehabilitation programs. The data set consists of 10 rehabilitation exercises. A sample of 10 healthy individuals repeated each exercise 10 times in front of two sensory systems for motion capturing: a Vicon optical tracker, and a Kinect camera. The data is presented as positions and angles of the body joints in the skeletal models provided by the Vicon and Kinect mocap systems.

Wearables or infrastructure sensors have been widely proposed for automated tracking and analysis of individual-level exercise activities. This dataset is collected as part of building a pervasive, low-cost digital personal trainer system, that supports fine-grained tracking of an individual’s free-weights exercises via a combination of (a) sensors on personal wireless ear-worn devices (‘earables’) and (b) inexpensive IoT sensors attached to exercise equipment (e.g., dumbbells). The dataset is comprised of sensor signals acquired from two 6-axis IMUs and contains a total of 324 samples for 3 different free-weight exercises performed by 27 individuals.

IntroductionExercising at home is an accessible alternative to the gym, although it presents challenges such as low adherence, poor quality and difficulties in reaching set goals. Wearable technologies and the use of heart rate variability (HRV) make it possible to personalise workouts, optimise fitness and improve adherence. However, specific exercise recommendations based on these metrics are still lacking. This study evaluated the impact of HRV-based training using the Selftraining UMH app in an autonomous format versus a Personal Trainer-led approach.MethodsSeventy sedentary adults were divided into three groups: Autonomous (n = 18), Personal Trainer (n = 23), and Control (n = 29). After a two-week baseline HRV assessment, participants underwent an 11-week intervention, with pre- and post-tests on peak oxygen uptake, aerobic power, total test time, strength, and HRV.ResultsBoth intervention groups completed similar session numbers (23.3 vs. 24.5) and high-intensity workouts (13.7 vs. 14.6). Both groups improved significantly (p 

Note 08/07/13: Errata for regarding two variables incorrectly labelled with the same description in the Data Archive for the Health Survey for England - 2008 dataset deposited in the UK Data Archive Author: Health and Social Care Information Centre, Lifestyle Statistics Responsible Statistician: Paul Eastwood, Lifestyles Section Head Version: 1 Original date of publication: 17th December 2009 Date of errata: 11th June 2013 · Two physical activity variables (NSWA201 and WEPWA201) in the Health Survey for England - 2008 dataset deposited in the Data Archive had the same description of 'on weekdays in the last week have you done any cycling (not to school)?'. This is correct for NSWA201, but incorrect for WEPWA201 · The correct descriptions are: · NSWA201 - 'on weekdays in the last week have you done any cycling (not to school)?' · WEPWA201 - 'on weekends in the last week have you done any cycling (not to school)?' · This has been corrected and the amended dataset has been deposited in the UK Data Archive. NatCen Social Research and the Health and Social Care Information Centre apologise for any inconvenience this may have caused. Note 18/12/09: Please note that a slightly amended version of the Health Survey for England 2008 report, Volume 1, has been made available on this page on 18 December 2009. This was in order to correct the legend and title of figure 13G on page 321 of this volume. The NHS IC apologises for any inconvenience caused. The Health Survey for England is a series of annual surveys designed to measure health and health-related behaviours in adults and children living in private households in England. The survey was commissioned originally by the Department of Health and, from April 2005 by The NHS Information Centre for health and social care. The Health Survey for England has been designed and carried out since 1994 by the Joint Health Surveys Unit of the National Centre for Social Research (NatCen) and the Department of Epidemiology and Public Health at the University College London Medical School (UCL). The 2008 Health Survey for England focused on physical activity and fitness. Adults and children were asked to recall their physical activity over recent weeks, and objective measures of physical activity and fitness were also obtained. A secondary objective was to examine results on childhood obesity and other factors affecting health, including fruit and vegetable consumption, drinking and smoking.

This submission includes the data from the Gender differences in exercise self-efficacy and outcome expectations for exercise in individuals with stroke study (GRIPS) study. The related publication can be found in the journal Plos One. The corresponding data has been uploaded via an Excel Spreadsheet.

Data source(s): - Sport England (https://www.sportengland.org/know-your-audience/data/active-lives/active-lives-data-tables) Dataset(s) used:- Exercise and Sports Levels (Children and Young People in school years 1-11)Statistic(s) used:Number of people who are active* less than an average of 30 minutes a day in the academic year 2017/18 (*moderate or vigorous physical activity).The datasets report statistics based on administrative areas (Districts). However, those areas’ boundaries have been generalised in this dataset. It is recommended that you overlay the official District administrative boundaries from the Living Atlas on top of this data, to view the accurate boundary lines.

This dataset includes both qualitative and quantitative data collected from coaches of athletes with intellectual disabilities and the athletes themselves.All data were gathered online or during the Special Olympics World Games Berlin 2023, an international event where over 7000 athletes with intellectual disabilities participated in various sports, supported by approximately 3000 coaches.Demographic data were initially collected from both athletes with intellectual disabilities and their coaches. Following this, participants engaged in two main data collection processes: (1) participation in semi-structured interviews, and (2) completion of questionnaires.1. Semi-Structured InterviewsSemi-structured interviews were conducted separately with coaches and athletes with intellectual disabilities.Coaches’ interviews focused on their coaching practices, the quality of their relationships with athletes, and the needs and challenges they face in promoting inclusive sports participation.Athletes’ interviews were designed using simple, clear, and accessible language. The questions explored their motivation for exercise, their relationships with coaches, and their perspectives on the challenges and needs related to inclusive sports participation.2. QuestionnairesCoaches’ Questionnaires:Interpersonal Behaviours Questionnaire – Self (IBQ-Self):This 24-item questionnaire measures coaches’ self-perceptions of their interpersonal behaviours within the framework of Self-Determination Theory. It includes six subscales assessing autonomy-supportive, autonomy-thwarting (controlling), competence-supportive, competence-thwarting, relatedness-supportive, and relatedness-thwarting behaviours. The scale has demonstrated strong psychometric properties, including internal consistency and construct validity in sport settings.Coach–Athlete Relationship Questionnaire (CART-Q):This 11-item measure evaluates coaches’ perceptions of their relationship with athletes across three dimensions: closeness, complementarity, and commitment. It is a widely used and validated tool that offers reliable insights into the quality of coach–athlete relationships.Athletes with intellectual disabilities Questionnaires:Pictorial Motivation Scale (PMS) in Physical Activity:Designed for individuals with intellectual disabilities who may not be able to read independently, this 16-item pictorial scale assesses exercise motivation across four subscales: intrinsic motivation, non-self-determined extrinsic motivation, self-determined extrinsic motivation, and amotivation. The scale is based on Self-Determination Theory and has demonstrated adequate validity and reliability for this population.Mini MANS-LD:This 9-item self-report measure assesses quality of life among individuals with intellectual disabilities. It was designed to be conceptually relevant, easy to administer, and suitable for individuals with varying levels of communication abilities.SPAIM (Single-Item Physical Activity Intention Measure):This one-item measure evaluates the intention of individuals with intellectual disabilities to engage in physical activity. It offers a quick and validated method for capturing participants’ PA intentions.*** Questionnaire athletes dataset2: 'The order of the demographic data has been corrected and the scores of the Quality of Life questionnaire have been reversed.

This report presents information on obesity, physical activity and diet drawn together from a variety of sources for England. More information can be found in the source publications which contain a wider range of data and analysis. Each section provides an overview of key findings, as well as providing links to relevant documents and sources. Some of the data have been published previously by NHS Digital. A data visualisation tool (link provided within the key facts) allows users to select obesity related hospital admissions data for any Local Authority (as contained in the data tables), along with time series data from 2013/14. Regional and national comparisons are also provided. The report includes information on: Obesity related hospital admissions, including obesity related bariatric surgery. Obesity prevalence. Physical activity levels. Walking and cycling rates. Prescriptions items for the treatment of obesity. Perception of weight and weight management. Food and drink purchases and expenditure. Fruit and vegetable consumption. Key facts cover the latest year of data available: Hospital admissions: 2018/19 Adult obesity: 2018 Childhood obesity: 2018/19 Adult physical activity: 12 months to November 2019 Children and young people's physical activity: 2018/19 academic year