This dataset provides a detailed overview of gym members' exercise routines, physical attributes, and fitness metrics. It contains 973 samples of gym data, including key performance indicators such as heart rate, calories burned, and workout duration. Each entry also includes demographic data and experience levels, allowing for comprehensive analysis of fitness patterns, athlete progression, and health trends.

Key Features:

• Age: Age of the gym member.
• Gender: Gender of the gym member (Male or Female).
• Weight (kg): Member’s weight in kilograms.
• Height (m): Member’s height in meters.
• Max_BPM: Maximum heart rate (beats per minute) during workout sessions.
• Avg_BPM: Average heart rate during workout sessions.
• Resting_BPM: Heart rate at rest before workout.
• Session_Duration (hours): Duration of each workout session in hours.
• Calories_Burned: Total calories burned during each session.
• Workout_Type: Type of workout performed (e.g., Cardio, Strength, Yoga, HIIT).
• Fat_Percentage: Body fat percentage of the member.
• Water_Intake (liters): Daily water intake during workouts.
• Workout_Frequency (days/week): Number of workout sessions per week.
• Experience_Level: Level of experience, from beginner (1) to expert (3).
• BMI: Body Mass Index, calculated from height and weight.

This dataset is ideal for data scientists, health researchers, and fitness enthusiasts interested in studying exercise habits, modeling fitness progression, or analyzing the relationship between demographic and physiological data. With a wide range of variables, it offers insights into how different factors affect workout intensity, endurance, and overall health.

In 2023, it was found that 22.4 percent of men in the United States participated in sports, exercise, and recreational activities daily, compared to only 19.9 percent of women. These statistics highlight a notable difference in the daily engagement of different genders in sporting activities. Other factors influencing this participation include socioeconomic status, age, disability, ethnicity, geography, personal interests, and societal expectations. These barriers can prevent individuals from having equal access to, and opportunities for, sport participation. What role does gender play in sports participation? Historically, many sports have been segregated by gender, with men and women participating in separate leagues and competitions. This segregation has led to a lack of opportunities for women and girls to participate in sports at the same level as men and boys. Additionally, societal attitudes and stereotypes about gender can discourage women and girls from participating in sports or limit their access to resources and support for their athletic pursuits. This often results in fewer women and girls participating in sports and a lack of representation of women and girls in leadership roles within the sports industry. However, in recent years, there has been an increased focus on promoting gender equality in sports and providing equal opportunities for men and women to participate in sports. This includes initiatives to increase funding and support for women's sports, as well as efforts to challenge gender stereotypes and discrimination in the athletic world. Impact of the COVID-19 pandemic on sports participation The COVID-19 pandemic led to many people spending more time at home due to lockdowns, remote work, and school closures. This resulted in many people having more time to engage in sports and other physical activities, as seen in the share of the U.S. population engaged in sports and exercise peaking in 2020. With gyms and sports facilities closed or with limited access, many people turned to home-based workouts and other activities. This included activities such as running, cycling, and strength training that could all be done at home with minimal equipment. Online classes and streaming services also saw an increase in usage during the pandemic, providing people with access to a wide range of workout options and fitness programs.

This statistic shows how often people in the United States work out at their gym in 2016 according to a Statista survey. ** percent of survey respondents said they work out at their gym several times a week.

This project focuses on analyzing human body movements during common exercises by capturing and processing angles of key body joints. We utilized video data to extract frame-by-frame angles of the following body parts during various exercises such as push-ups, jumping jacks, pull-ups, squats, and Russian twists. For pose estimation, MediaPipe was used to detect body landmarks, while YOLOv6 was employed for object detection to enhance accuracy.

Methodology

• Video Collection: Videos were recorded for each exercise (push-ups, jumping jacks, pull-ups, squats, Russian twists), ensuring proper form and variety in movement.
• Frame-by-Frame Analysis: Each video was processed frame by frame, and landmarks were detected using MediaPipe's Pose Estimation. We calculated the angles of key joints by using the positional data of landmarks across different frames.
• Object Detection with YOLOv6: YOLOv6 was used to identify specific objects and enhance the robustness of the pose estimation by detecting outliers or incorrect poses during exercises, thereby improving the accuracy of the analysis.

Applications This dataset can be used for multiple applications: - Form Correction: By comparing these angles with standard benchmarks, feedback can be provided to improve exercise form. - Performance Tracking: Over time, users can monitor their improvement by analyzing the changes in their joint angles during exercises. - Pose Classification: Machine learning models can be trained to classify correct vs. incorrect form, enabling the development of smart fitness assistants. - Real-time Feedback Systems: Using pose estimation in conjunction with live video, real-time systems can be developed to guide users during workouts.

Exercises Analyzed The following exercises were captured and analyzed for this dataset:

• Push-ups: Key focus on shoulder, elbow, and hip angles.
• Jumping Jacks: Full-body motion tracked via shoulder, elbow, hip, knee, and ankle angles.
• Pull-ups: Primarily focused on shoulder and elbow joint movements.
• Squats: Analyzed hip, knee, and ankle angles for depth and posture analysis.
• Russian Twists: Core movement tracked via shoulder and hip angles to assess rotational motion.

Potential Analysis - Time-Series Analysis: The data can be treated as a time-series, allowing for the identification of trends in joint movement over the duration of an exercise. - Pose Optimization: Optimization models can be used to suggest improvements in form based on angle analysis. - Machine Learning Integration: The dataset can serve as input for machine learning algorithms to automate form correction and workout optimization.

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.

Explore this extensive dataset on daily exercise and fitness metrics, designed to provide valuable insights into individuals' exercise routines, health status, and fitness progress. With a rich collection of attributes, this dataset enables researchers, fitness enthusiasts, and health professionals to analyze and draw meaningful conclusions about exercise patterns and their impact on overall well-being.

Note:

Because this dataset has been used in a competition, we had to hide some of the data to prepare the test dataset for the competition. Thus, in the previous version of the dataset, only train.csv file is existed.

Content

This dataset represents 10 different physical poses that can be used to distinguish 5 exercises. The exercises are Push-up, Pull-up, Sit-up, Jumping Jack and Squat. For every exercise, 2 different classes have been used to represent the terminal positions of that exercise (e.g., “up” and “down” positions for push-ups).

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. From every video, at least 2 frames are manually extracted. The extracted frames represent the terminal positions of the exercise.

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.

https://mediapipe.dev/images/mobile/pose_tracking_full_body_landmarks.png" alt="33 pose landmarks">

A September 2023 survey on exercise habits in the United States revealed that around ** percent of Gen Z respondents worked out in a gym or health club. This number fell to ** percent among Baby Boomers.

This statistic shows why people in the United States work out at their gyms in 2016 according to a Statista survey. ** percent of survey respondents said that their gym helps them to stay healthy.

Analysis of ‘Fitness Trends Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aroojanwarkhan/fitness-data-trends on 28 January 2022.

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

Context

The motivation behind collecting this data-set was personal, with the objective of answering a simple question, “does exercise/working-out improve a person’s activeness?”. For the scope of this project a person’s activeness was the measure of their daily step-count (the number of steps they take in a day). Mood was measured in either "Happy", "Neutral" or "Sad" which were given numeric values of 300, 200 and 100 respectively. Feeling of activeness was measured in either "Active" or "Inactive" which were given numeric values of 500 and 0 respectively. I had noticed for a while that during the months when I was exercising regularly I felt more active and would move around a lot more. As opposed to when I was not working out, i would feel lethargic. I wanted to know for sure what the connection between exercise and activeness was. I started compiling the data on 6th October with the help Samsung Health application that was recording my daily step count and the number of calories burned. The purpose of the project was to establish through two sets of data (control and experimental) if working-out/exercise promotes an increase in the daily step-count or not.

Content

Date Step Count Calories Burned Mood Hours of Sleep Feeling or Activeness or Inactiveness Weight

Acknowledgements

Special thanks to Samsung Health that contributed to the set by providing daily step count and the number of calories burned.

Inspiration

"Does exercise/working-out improve a person’s activeness?”

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

let's break down each column in this fitness tracker app data:

1. UserID: This column contains unique identifiers for each user of the fitness tracker app. Each row corresponds to a specific user's data.

2. Date: This column represents the date on which the data was recorded or collected. It's likely in a date format (e.g., YYYY-MM-DD).

3. Steps: This column records the number of steps the user took on the given date. Steps are a common metric used by fitness trackers to measure physical activity.

4. Total_Distance: This column indicates the total distance covered by the user on the given date, likely measured in a unit such as kilometers or miles. It might be calculated based on steps taken and stride length.

5. Tracker_Distance: This column represents the distance recorded by the fitness tracker device itself, which could include steps as well as other factors like GPS data.

6. Logged_Activities_Distance: This column contains additional distance covered during specific activities that the user manually logged into the app. For example, if the user went for a run and entered the distance manually, it would be recorded here.

7. Very_Active_Distance: This column indicates the distance covered during activities classified as "very active," such as running, intense cardio, or high-intensity interval training.

8. Moderately_Active_Distance: This column represents the distance covered during activities classified as "moderately active," which may include brisk walking, cycling, or light jogging.

9. Light_Active_Distance: This column indicates the distance covered during activities classified as "light activity," such as casual walking, household chores, or light stretching.

10. Sedentary_Active_Distance: This column represents the distance covered while engaged in sedentary activities, such as sitting or lying down. It could be used to track inactive periods.

11. Very_Active_Minutes: This column records the number of minutes the user spent engaging in activities classified as "very active," typically high-intensity exercises that significantly elevate heart rate.

12. Fairly_Active_Minutes: This column contains the number of minutes spent engaging in activities classified as "fairly active," which are moderately intense activities that raise heart rate but are not as vigorous as "very active" activities.

13. Lightly_Active_Minutes: This column indicates the number of minutes spent engaging in activities classified as "lightly active," which include low-intensity activities that contribute to overall movement but do not significantly elevate heart rate.

14. Sedentary_Minutes: This column records the amount of time the user spent in sedentary behavior, such as sitting or lying down, without engaging in physical activity.

15. Calories_Burned: This column represents an estimate of the number of calories the user burned throughout the day based on their activity levels and other factors like age, weight, and gender. It's often calculated using algorithms that take into account activity data and user profile information.

This dataset contains historical open gym and open studio information. For current open gym schedules check out our website.This dataset is an archive - it is not being updated.

Workout Data

The dataset provided includes the logged data of my own strength workouts following the 5/3/1 BBB routine. While some insights were derived in an article I published recently, there is an opportunity for the community to benefit from the open sourcing of this data.

Most notably, I haven't found time to come up with a way of training and applying performance metrics against the data which I have labeled; and I'm hoping that the work I've spent to prepare a decent dataset can be picked up by someone looking to try out computer vision but on a dataset that has a clearer use case than some of the toy datasets that are currently open sourced.

The goal is to try to build an ML model that takes either phone images or scans of workout sheets, and automatically transfer them into the more structured Excel format for easier data gathering.

Content

There are 3 folders contained in the dataset, all files within the folder are datestamped by filename as DD-MM-YYYY: Excel Data This is considerable as the labeled data to a matching phone image or scanned image. There is an Excel file for each workout performed. Phone Images These are images of the filled out workout sheets as taken by my Android phone. More recently I have stopped taking phone images of my workout sheets, but about 85% of the Excel data has a matching phone image. While these images represent a harder challenge for computer vision, the ease of taking these images makes them much more practical as a future deployable mobile application. Scanned Images These are scans of the filled out workout sheets as scanned on my HP Deskjet printer. These scans are higher quality than the mobile images, however the lack of quick and easy access to scanners means that it is harder to gain a userbase as a potential future product.

Introduction

CrossFit Statistics: CrossFit is a high-intensity fitness program that combines functional movements, strength training, and cardiovascular exercises to improve overall fitness.

It emphasizes varied workouts (Workouts of the Day or WODs) that target endurance, strength, flexibility, and power and can be scaled to suit all fitness levels.

CrossFit incorporates exercises like Olympic lifts, gymnastics movements, and bodyweight exercises, often structured in formats like AMRAP, EMOM, or Tabata.

Known for its strong community support, CrossFit offers benefits such as improved physical conditioning, weight loss, and mental toughness, though it requires proper technique to minimize injury risk.

The raw data on behavior and physical fitness. The behavior for sampling worker before joining WE is on sheet behavior 31 and 62 Then, we show all data for behavior and physical fitness.

A September 2023 survey on exercise habits in the United States revealed that around 65 percent of male respondents took part in strength training. Meanwhile, just under one quarter of female respondents participated in yoga.

Snapshot img

The smart fitness market share is expected to increase by USD 34.06 billion from 2021 to 2026, at a CAGR of 13.33%.

This smart fitness market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers smart fitness market segmentation by product (gear, smart bike, ellipticals, treadmill, and others) and geography (North America, Europe, APAC, MEA, and South America). The smart fitness market report also offers information on several market vendors, including Alphabet Inc., Apple Inc., Dyaco International Inc., Fossil Group Inc., Garmin Ltd., Johnson Health Tech, Nautilus Inc., Peloton Interactive Inc., Tunturi New Fitness BV, and Zwift Inc. among others.

What will the Smart Fitness Market Size be During the Forecast Period?

Download the Free Report Sample to Unlock the Smart Fitness Market Size for the Forecast Period and Other Important Statistics

Smart Fitness Market: Key Drivers and Trends

The increasing focus on fitness and a healthy lifestyle orientation is notably driving the smart fitness market growth, although factors such as lack of data privacy and security may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the smart fitness industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Smart Fitness Market Driver

One of the key factors driving growth in the smart fitness market is the increasing focus on fitness and healthy lifestyle orientation. The rising adoption of a sedentary lifestyle is exposing people to the high risk of developing various health conditions, such as anxiety, obesity, type 2 diabetes, and osteoporosis. The hectic work schedules and increasing health issues have forced people to undertake some form of exercise daily to remain healthy and prevent various health-related issues. Thus, increasing awareness about the importance of a healthy lifestyle has led to a rise in the demand for various fitness activities, including interactive fitness. Interactive fitness activities offer several benefits, such as body coordination and the strengthening of the abdominal muscles. Promotional activities conducted by major vendors operating in the global smart fitness market to generate smart fitness products awareness have played a key role in driving the demand for interactive fitness. The wellness services industry, which also includes fitness services and a healthy lifestyle, has witnessed significant growth in the last five years. It is expected to achieve strong growth during the forecast period, owing to the increasing focus of employees on health and fitness.

Key Smart Fitness Market Challenge

The lack of data privacy and security will be a major challenge for the smart fitness market during the forecast period. Smart wearable devices can cause work interruption for users as they store a huge amount of sensitive information. Smart wearable devices also use GPS navigation systems for receiving location-based information, and at times, individuals have to share their location to get certain information. This information can also be retrieved and used by several advertisers. Security breaches can also occur because of the use of innovative technologies in these devices. The leakage of data stored in the sports wearable devices of renowned sportspersons and athletes can lead to serious security threats. The information about a subscriber's location is owned and controlled by the respective network operators of mobile carriers and mobile content providers. With network operators privy to such information, end-users are concerned about their privacy and security, in spite of the legal framework to protect it.

This smart fitness market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2022-2026.

Who are the Major Smart Fitness Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Alphabet Inc.
Apple Inc.
Dyaco International Inc.
Fossil Group Inc.
Garmin Ltd.
Johnson Health Tech
Nautilus Inc.
Peloton Interactive Inc.
Tunturi New Fitness BV
Zwift Inc.

This statistical study of the smart fitness market encompasses successful business strategies deployed by the key vendors. The smart fitness market is fragmented and the vendors are deploying growth strategies such as increasing their R&D investments to compete in the market.

To make the most of the opportunities and recover from post C

Number and percentage of adults being moderately active or active during leisure time, by age group and sex.

Employment statistics on the Gym & Fitness Franchises industry in the US

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Connected Gym Equipment Market Size and Forecast 2025-2029

The connected gym equipment market size estimates the market to reach USD 10.16 billion, at a CAGR of 42.4% between 2024 and 2029. North America is expected to account for 39% of the growth contribution to the global market during this period. In 2019, the CTE segment was valued at USD 531.90 billion and has demonstrated steady growth since then.

The market is experiencing significant growth, driven by the increasing penetration of smartphones and the rising demand for connected gym services. Consumers are seeking convenience and personalized fitness experiences, leading to a surge in demand for technology-enabled gym equipment. However, this market faces challenges as well. Compatibility with various mobile operating systems is essential to cater to a diverse user base, making it crucial for manufacturers to ensure their equipment is adaptable. Another obstacle is the lack of awareness regarding gym-related technology and connected equipment among potential customers, necessitating marketing efforts to educate and engage consumers.
Companies in this market must navigate these challenges while capitalizing on the growing demand for connected fitness solutions to remain competitive and thrive in the evolving landscape.

What will be the Size of the Connected Gym Equipment Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, integrating advanced technologies to enhance user experiences and optimize fitness outcomes. Strength training metrics are no longer limited to manual tracking; IoT fitness ecosystems now enable real-time workout feedback through exercise video streaming and API integration. Home gym connectivity, workout scheduling systems, and wearable device sync facilitate convenience and consistency. Body composition analysis, data encryption protocols, fitness app integration, sleep tracking integration, and user activity dashboards offer comprehensive insights into overall health and progress. Virtual fitness classes, personalized training plans, and augmented reality training cater to diverse fitness goals. Machine learning algorithms and biometric data capture enable AI-powered fitness guidance, while cloud data storage ensures accessibility.

One notable example of market innovation is a fitness platform that experienced a 50% increase in user engagement through the integration of real-time workout feedback and customized workout routines. Industry growth is expected to reach double-digit percentages as the market unfolds, incorporating features like community fitness features, virtual reality fitness, gamified fitness programs, secure user authentication, remote fitness coaching, equipment maintenance alerts, and cardio performance analysis.

How is this Connected Gym Equipment Industry segmented?

The connected gym equipment industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  CTE
  STE


End-user

  Residential
  Commercial


Distribution Channel

  Online
  Offline


Type

  Cardio
  Strength Training


Technology Specificity

  IoT
  AI
  Bluetooth


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The CTE segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth due to the fusion of technology and fitness. Strength training metrics and cardio performance analysis enable users to track their progress and optimize workouts. Exercise video streaming and virtual fitness classes offer immersive and personalized training experiences. Home gym connectivity and workout scheduling systems ensure harmonious integration of equipment and routines. API integration, fitness app integration, and wearable device sync facilitate seamless data transfer and analysis. Body composition analysis, sleep tracking integration, and user activity dashboards provide holistic health insights. Real-time workout feedback, progress visualization tools, and personalized training plans cater to individual fitness goals.

Exercise equipment sensors, customized workout routines, and augmented reality training offer engaging and effective workouts. Digital fitness subscription models provide affordable access to a wide range of features. Community fitness features foster a supportive and motivating environment