100+ datasets found
  1. Gym Members Exercise Dataset

    • kaggle.com
    Updated Oct 6, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    vala khorasani (2024). Gym Members Exercise Dataset [Dataset]. https://www.kaggle.com/datasets/valakhorasani/gym-members-exercise-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 6, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    vala khorasani
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    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.

  2. U.S. fitness center/health club memberships 2000-2024

    • statista.com
    Updated May 23, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). U.S. fitness center/health club memberships 2000-2024 [Dataset]. https://www.statista.com/statistics/236123/us-fitness-center-health-club-memberships/
    Explore at:
    Dataset updated
    May 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The number of members of fitness centers and health clubs within the United States has experienced a near continual increase between 2000 and 2024. In 2024, there were found to be around ** million members of fitness centers and health clubs within the U.S., the greatest number during the period of observation.

  3. Gym membership increase in the U.S. 2010-2019, by age

    • statista.com
    Updated Oct 27, 2022
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2022). Gym membership increase in the U.S. 2010-2019, by age [Dataset]. https://www.statista.com/statistics/246984/obstacles-to-joining-a-health-club/
    Explore at:
    Dataset updated
    Oct 27, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    One of the most effective ways to stay in shape is to take part in regular workouts at the gym. Between 2010 and 2019, the number of health and fitness club members aged 65 or older increased by 34.16 percent, while this increase stood at 69.81 percent among those aged six to 17.

  4. d

    Data from: Open Gym

    • catalog.data.gov
    • data.townofcary.org
    • +5more
    Updated Oct 19, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Cary (2024). Open Gym [Dataset]. https://catalog.data.gov/dataset/open-gym
    Explore at:
    Dataset updated
    Oct 19, 2024
    Dataset provided by
    Cary
    Description

    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.

  5. Gym member share in the U.S. in 2019, by age

    • statista.com
    Updated Oct 27, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2022). Gym member share in the U.S. in 2019, by age [Dataset]. https://www.statista.com/statistics/1244812/gym-member-share-age/
    Explore at:
    Dataset updated
    Oct 27, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    United States
    Description

    One of the most effective ways to stay in shape is to take part in regular workouts at the gym. In the last ten years, there has been a significant increase in health and fitness club memberships among children and adolescents, and under 20s made up 16 percent of total gym members in the United States in 2019.

  6. Gym, Health & Fitness Clubs in the US - Market Research Report (2015-2030)

    • ibisworld.com
    Updated May 15, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    IBISWorld (2025). Gym, Health & Fitness Clubs in the US - Market Research Report (2015-2030) [Dataset]. https://www.ibisworld.com/united-states/market-research-reports/gym-health-fitness-clubs-industry/
    Explore at:
    Dataset updated
    May 15, 2025
    Dataset authored and provided by
    IBISWorld
    License

    https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/

    Time period covered
    2015 - 2030
    Area covered
    United States
    Description

    Gym, health and fitness clubs stand at a dynamic crossroads, shaped by both impressive resilience and evolving consumer expectations. Despite economic headwinds—including persistent inflation, rising membership fees and supply chain disruptions—Americans’ appetite for fitness hasn’t waned. While higher prices and tariff-driven equipment costs have prompted some concerns around affordability and retention, leading operators have kept pace by doubling down on transparency, technological innovation and community-driven experiences, keeping the industry remarkably buoyant, even as members become more discerning and hybrid workout habits take root. Revenue has expanded at a CAGR of 7.1% to $45.7 billion in 2025, including an uptick of 2.0% that year. Home workouts and digital fitness surged in recent years, with brands like Peloton, Apple Fitness and countless app-based platforms filling the void. Still, the desire for social connection, accountability and access to specialized classes supported attendance at gyms and fitness centers, with group classes, boutique experiences and sports leagues (like the nation’s pickleball boom) fueling a new wave of growth. Technological integration has become standard, as fitness centers capitalized on mobile booking, wearables, hybrid class offerings and personalized digital experiences to boost retention. Gyms have also responded to sticky inflation and financial uncertainty by offering more flexible, tiered memberships and novel pay-per-visit plans, making fitness accessible across a wider range of budgets and life stages, boosting profit. Gym, health and fitness clubs will deepen their shift into a wellness-centric, tech-enabled ecosystem, with opportunities and challenges in equal measure. Demographic tailwinds will prove significant: as the population ages and healthcare costs climb, older adults will turn to gyms for exercise as well as holistic health management. Gyms, health and fitness centers are shifting toward integrated, medically informed offerings, blending classes with diagnostics, tracking devices and partnerships with healthcare providers. Affordability, digital convenience and privacy will be crucial considerations as gyms race to balance premium health solutions with accessibility. Gyms and fitness centers that innovate around flexibility and evidence-based care will sustain growth. Revenue is expected to grow at a CAGR of 1.4% to reach an estimated $49.1 billion by 2030.

  7. Gym, Health & Fitness Clubs in the US

    • ibisworld.com
    Updated Feb 1, 2002
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    IBISWorld (2002). Gym, Health & Fitness Clubs in the US [Dataset]. https://www.ibisworld.com/industry-statistics/number-of-businesses/gym-health-fitness-clubs-united-states/
    Explore at:
    Dataset updated
    Feb 1, 2002
    Dataset authored and provided by
    IBISWorld
    License

    https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/

    Time period covered
    2005 - 2031
    Area covered
    United States
    Description

    Number of Businesses statistics on the Gym, Health & Fitness Clubs industry in the US

  8. Gym User Dropout Prediction Dataset

    • kaggle.com
    Updated Aug 3, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Hassan Abdul-razeq (2025). Gym User Dropout Prediction Dataset [Dataset]. https://www.kaggle.com/datasets/hassanabdulrazeq/gym-user-dropout-prediction-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 3, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Hassan Abdul-razeq
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Gym User Dropout Prediction Dataset

    Description

    This synthetic dataset simulates user behavior in a fitness application, designed to predict the risk of gym membership dropout based on attendance patterns and personal attributes. The dataset contains 10,000 realistic user profiles with features that influence gym retention, making it ideal for classification tasks in behavioral analytics.

    Key Features

    • Realistic distributions matching actual gym user behavior patterns
    • Complex feature interactions that simulate real-world decision-making
    • Controlled noise to mimic natural data variability
    • Balanced classes for effective machine learning modeling

    Potential Use Cases

    • Predicting at-risk users for retention interventions
    • Analyzing factors contributing to gym commitment
    • Developing personalized workout recommendations
    • Behavioral segmentation of fitness app users

    Dataset Characteristics

    • Number of instances: 10,000
    • Number of features: 8 predictive + 1 target
    • Missing values: No
    • Synthetic but realistic: Yes

    Columns Description

    FeatureTypeDescriptionValue Range
    user_idintUnique user identifier1-10000
    ageintUser's age18-60 (peaked at 25-40)
    gendercategoricalUser's genderMale/Female
    sessions_per_weekintWeekly gym attendance0-7 sessions
    avg_session_durationfloatAverage workout length in minutes10-120
    progress_scorefloatComposite fitness progress metric0-100
    mood_aftercategoricalPost-workout emotional stateEnergized/Neutral/Fatigued
    injurycategoricalReported workout injuriesNone/Knee/Back/Shoulder
    dropoutbinaryTarget variable - quit status0 (active)/1 (quit)

    Generation Methodology

    Data was programmatically generated with: 1. Base distributions matching real gym statistics 2. Logical correlations between features (e.g., more sessions → longer durations) 3. Non-linear relationships in target variable 4. Controlled noise injection (Gaussian + categorical variability)

    Suggested Evaluation Metrics

    For classification models: - Precision-Recall curves (class imbalance consideration) - F1 score - ROC AUC - Feature importance analysis

    License

    CC0: Public Domain (Free to use for any purpose)

    Acknowledgements

    Synthetic dataset created for machine learning education and benchmarking purposes. Inspired by real fitness app analytics challenges.

    Dataset Link

    gym_user_dropout_dataset.csv

  9. h

    Coq-Gym-Data-Set

    • huggingface.co
    Updated Aug 29, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Brando Miranda (2024). Coq-Gym-Data-Set [Dataset]. https://huggingface.co/datasets/brando/Coq-Gym-Data-Set
    Explore at:
    Dataset updated
    Aug 29, 2024
    Authors
    Brando Miranda
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    Proverbot Scrapes

    Here we include a dump of proofs in coq-gym using the proverbot9001 tool.

      Dataset Structure:
    

    relevant_lemmas: A list of lemmas that are relevant to the proof. Each lemma is accompanied by its formal statement and registration in the proof context. prev_tactics: The tactics used in the previous steps of the proof (The first tactic being the lemma definition itself). This can help in understanding the sequence of operations leading to the current state.… See the full description on the dataset page: https://huggingface.co/datasets/brando/Coq-Gym-Data-Set.

  10. Gym And Health Clubs Market Analysis, Size, and Forecast 2025-2029: North...

    • technavio.com
    pdf
    Updated Jan 4, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Technavio (2025). Gym And Health Clubs Market Analysis, Size, and Forecast 2025-2029: North America (US and Canada), APAC (China, India, Japan, South Korea), Europe (France, Germany, Italy, UK), Middle East and Africa (UAE), and South America [Dataset]. https://www.technavio.com/report/gym-and-health-clubs-market-industry-analysis
    Explore at:
    pdfAvailable download formats
    Dataset updated
    Jan 4, 2025
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2025 - 2029
    Area covered
    Canada, United Kingdom, United States
    Description

    Snapshot img

    Gym And Health Clubs Market Size 2025-2029

    The gym and health clubs market size is forecast to increase by USD 21.47 billion, at a CAGR of 3.9% between 2024 and 2029.

    The market is experiencing significant growth due to the increasing trend of health and wellness consciousness among demographic groups, particularly baby boomers and millennials. This demographic shift is driving the demand for comprehensive fitness and wellness services, as these generations prioritize maintaining an active lifestyle and overall health. However, this market growth also presents challenges. The dearth of a trained workforce capable of delivering high-quality services poses a significant obstacle. With the growing demand for personalized and effective fitness programs, health clubs face the challenge of recruiting and retaining a skilled workforce. This shortage of trained professionals can negatively impact the quality of services offered and potentially hinder market expansion.
    To capitalize on the market's opportunities and navigate these challenges, gym and health clubs must focus on investing in workforce development and training programs. This investment in human capital will not only help meet the growing demand for personalized services but also differentiate clubs from competitors. Additionally, strategic partnerships with educational institutions and industry organizations can provide a steady stream of qualified candidates, ensuring a skilled workforce to deliver top-notch services and drive business growth.
    

    What will be the Size of the Gym And Health Clubs 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.
    Request Free Sample

    The gym and health club market continues to evolve, with dynamic market activities unfolding across various sectors. Customer retention remains a top priority, leading to the implementation of personalized email marketing campaigns and community engagement initiatives. Wearable technology, such as smart scales and heart rate monitors, enables members to track their progress and stay motivated. Flexibility training, including the use of foam rollers and yoga mats, complements cardio equipment and strength training programs. Profit margins are maximized through space optimization and the offering of specialized fitness programs, such as rehabilitation services and senior fitness programs. Home gym equipment and virtual fitness classes cater to members' varying schedules and preferences.

    Environmental sustainability is a growing concern, with gym management prioritizing energy efficiency, waste reduction, and facility design. Staff management is crucial for providing excellent member experiences and ensuring safety regulations are met. Fitness apps, group exercise classes, and online fitness platforms offer convenience and flexibility. Liability insurance, injury prevention, and safety regulations are essential considerations for gym operators. Functional fitness and athletic training programs cater to competitive sports enthusiasts, while personal training and physical therapy services address individual needs. Fitness assessments, nutritional counseling, and class scheduling tools streamline operations and enhance the overall member experience. Marketing strategies, such as social media marketing and referral programs, help attract and retain new members.

    Operating costs are minimized through gym management software, facility maintenance, and cost-effective equipment, such as resistance bands and jump ropes. Spin classes and strength training remain popular offerings, while safety regulations and cleaning protocols ensure a clean and safe environment for all members.

    How is this Gym And Health Clubs Industry segmented?

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

    Service
    
      Membership fees
      Personal training and instruction fees
      Total admission fees
    
    
    Type
    
      Private
      Public
    
    
    Membership Type
    
      Monthly
      Annual
    
    
    End-User
    
      Individuals
      Corporates
    
    
    Geography
    
      North America
    
        US
        Canada
    
    
      Europe
    
        France
        Germany
        Italy
        UK
    
    
      Middle East and Africa
    
        UAE
    
    
      APAC
    
        China
        India
        Japan
        South Korea
    
    
      Rest of World (ROW)
    

    By Service Insights

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

    In the dynamic gym and health club market, customer retention is a top priority. Personal injury claims and insurance requirements are significant considerations, necessitating a focus on safety regulations and facility design. Cardio equipment, such as smart scales and energy-efficient

  11. Gyms in the UK by type, 2007-2025

    • statista.com
    Updated Feb 26, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Christina Gough (2025). Gyms in the UK by type, 2007-2025 [Dataset]. https://www.statista.com/topics/3411/fitness-industry-in-the-united-kingdom-uk/
    Explore at:
    Dataset updated
    Feb 26, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Christina Gough
    Area covered
    United Kingdom
    Description

    In 2025, there were over 7,200 gyms across the United Kingdom, of which over 4,700 were private facilities. In that same year, membership at gyms and fitness clubs across the UK stood at 11.3 million.

  12. Connected Gym Equipment Market Analysis, Size, and Forecast 2025-2029: North...

    • technavio.com
    pdf
    Updated Dec 27, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Technavio, Connected Gym Equipment Market Analysis, Size, and Forecast 2025-2029: North America (Canada), Europe (France, Germany, Italy, Spain, UK), APAC (China, India, Japan, South Korea), Middle East and Africa (UAE), and South America (Brazil) [Dataset]. https://www.technavio.com/report/connected-gym-equipment-market-industry-analysis
    Explore at:
    pdfAvailable download formats
    Dataset updated
    Dec 27, 2024
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2025 - 2029
    Area covered
    United Kingdom, Canada
    Description

    Snapshot img

    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.
    Request Free Sample

    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. Virtual reali

  13. C

    Global High-Value Low-Price (HVLP) Gyms Market Research and Development...

    • statsndata.org
    excel, pdf
    Updated Jul 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Stats N Data (2025). Global High-Value Low-Price (HVLP) Gyms Market Research and Development Focus 2025-2032 [Dataset]. https://www.statsndata.org/report/high-value-low-price-hvlp-gyms-market-272325
    Explore at:
    excel, pdfAvailable download formats
    Dataset updated
    Jul 2025
    Dataset authored and provided by
    Stats N Data
    License

    https://www.statsndata.org/how-to-orderhttps://www.statsndata.org/how-to-order

    Area covered
    Global
    Description

    The High-Value Low-Price (HVLP) gyms market represents a dynamic segment of the fitness industry, catering to an increasingly cost-conscious consumer base seeking quality fitness experiences at affordable prices. HVLP gyms have emerged as a compelling solution for individuals who want to achieve their fitness goals

  14. Germany: Gym and Fitness Equipment 2007-2024

    • app.indexbox.io
    Updated Jun 18, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    IndexBox AI Platform (2021). Germany: Gym and Fitness Equipment 2007-2024 [Dataset]. https://app.indexbox.io/table/950691/276/
    Explore at:
    Dataset updated
    Jun 18, 2021
    Dataset provided by
    IndexBox
    Authors
    IndexBox AI Platform
    License

    Attribution-NoDerivs 3.0 (CC BY-ND 3.0)https://creativecommons.org/licenses/by-nd/3.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2007 - Dec 31, 2024
    Area covered
    Germany
    Description

    Statistics illustrates consumption, production, prices, and trade of Gym and Fitness Equipment in Germany from 2007 to 2024.

  15. D

    Fitness, Club and Gym Management Software System Market Report | Global...

    • dataintelo.com
    csv, pdf, pptx
    Updated Dec 3, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Dataintelo (2024). Fitness, Club and Gym Management Software System Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/fitness-club-and-gym-management-software-system-market
    Explore at:
    csv, pptx, pdfAvailable download formats
    Dataset updated
    Dec 3, 2024
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Fitness, Club and Gym Management Software System Market Outlook



    The global Fitness, Club, and Gym Management Software System market size was valued at approximately $1.5 billion in 2023 and is anticipated to expand at a compound annual growth rate (CAGR) of 10.2% from 2024 to 2032, reaching a projected market size of $3.8 billion by 2032. The growth of this market is largely driven by the increasing awareness of health and fitness, coupled with the rapid technological advancements in the fitness industry. As more individuals prioritize their health and wellness, the demand for efficient management of fitness facilities has surged, propelling the adoption of comprehensive software solutions designed to streamline operations and enhance customer experience.



    An increasing number of fitness centers and gyms are operating globally, and this expanding market necessitates sophisticated management systems to ensure seamless operations. The integration of technology into fitness management not only helps in maintaining client engagement through personalized services but also aids in the efficient handling of administrative tasks. This growing trend among fitness facilities to digitize their operations and the increasing availability of cloud-based solutions serve as significant growth factors for the market. Moreover, with the rise of digital fitness platforms, there is a complementary need for robust management software to handle the logistics of hybrid fitness models, which combine physical and digital workouts.



    The rising trend of personalized fitness experiences is another key driver for the market. Fitness enthusiasts today seek customized workout routines and health tracking, which necessitates software systems capable of providing such bespoke services. These systems allow for the collection and analysis of user data, which can then be utilized to tailor fitness programs to individual needs, thus enhancing customer satisfaction and retention. Additionally, advanced analytics offered by these systems provide gym owners with valuable insights into their business operations, enabling them to make informed decisions and optimize resource allocation effectively.



    Furthermore, the aftermath of the COVID-19 pandemic has accelerated the digital transformation of the fitness industry. With temporary closures and social distancing protocols, many gym and fitness studio owners have shifted to virtual platforms to keep members engaged. This shift has underscored the importance of having a robust gym management software system in place, capable of handling both in-person and virtual sessions, managing memberships, scheduling, and processing payments efficiently. This trend is expected to continue in the coming years, further boosting the market growth.



    Regionally, North America currently holds the largest market share due to the high number of fitness centers and the early adoption of digital solutions in this region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth is fueled by increasing urbanization, rising disposable incomes, and a growing awareness of health and fitness among the population. Countries like China and India are seeing a surge in the number of fitness clubs and gym-goers, further driving the demand for efficient management systems.



    Component Analysis



    The Fitness, Club, and Gym Management Software System market is segmented into components, namely software and services. The software segment is the cornerstone of this market, offering solutions that automate various tasks and facilitate seamless management of gym and fitness club operations. The software provides features such as membership management, class scheduling, billing, and analytics, all of which are vital for the efficient day-to-day functioning of these facilities. As fitness centers strive to enhance member experiences and streamline operations, the demand for comprehensive software solutions continues to grow exponentially.



    Within the software segment, customization and integration capabilities are key differentiators. Gym owners increasingly prefer solutions that can be tailored to meet their specific needs and easily integrated with existing systems. Such software solutions allow fitness centers to provide a personalized experience to their members, thereby increasing customer satisfaction and retention rates. Additionally, the growing emphasis on data security and privacy has led to the development of software systems with advanced security features, offering peace of mind to both gym operators and their clientele.

    &l

  16. Central Asia: Gym and Fitness Equipment 2007-2024

    • app.indexbox.io
    Updated Dec 16, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    IndexBox AI Platform (2024). Central Asia: Gym and Fitness Equipment 2007-2024 [Dataset]. https://app.indexbox.io/table/950691/143/
    Explore at:
    Dataset updated
    Dec 16, 2024
    Dataset provided by
    IndexBox
    Authors
    IndexBox AI Platform
    License

    Attribution-NoDerivs 3.0 (CC BY-ND 3.0)https://creativecommons.org/licenses/by-nd/3.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2007 - Dec 31, 2024
    Area covered
    Central Asia
    Description

    Statistics illustrates consumption, production, prices, and trade of Gym and Fitness Equipment in Central Asia from 2007 to 2024.

  17. Gym membership increase in the U.S. 2010-2019, by gender

    • statista.com
    Updated Oct 27, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2022). Gym membership increase in the U.S. 2010-2019, by gender [Dataset]. https://www.statista.com/statistics/246974/reasons-for-coming-back-to-the-health-club-one-is-member-of/
    Explore at:
    Dataset updated
    Oct 27, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    One of the most effective ways to stay in shape is to take part in regular workouts at the gym. Between 2010 and 2019, the number of female health and fitness club members increased by 32.2 percent, while this increase stood at 23.2 percent among male gym-goers.

  18. Exercise Detection dataset

    • kaggle.com
    Updated Sep 22, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    MRIGAANK JASWAL (2024). Exercise Detection dataset [Dataset]. https://www.kaggle.com/datasets/mrigaankjaswal/exercise-detection-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 22, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    MRIGAANK JASWAL
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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.

  19. daily exercise dataset

    • kaggle.com
    Updated Sep 7, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Sumit Kumbhkarn (2023). daily exercise dataset [Dataset]. https://www.kaggle.com/datasets/sumitkumbhkarn/daily-exercise-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 7, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Sumit Kumbhkarn
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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.

  20. F

    Producer Price Index by Industry: Fitness and Recreational Sports Centers

    • fred.stlouisfed.org
    json
    Updated Sep 10, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2025). Producer Price Index by Industry: Fitness and Recreational Sports Centers [Dataset]. https://fred.stlouisfed.org/series/PCU713940713940
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Sep 10, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Producer Price Index by Industry: Fitness and Recreational Sports Centers (PCU713940713940) from Dec 2004 to Aug 2025 about fitness, sport, recreation, PPI, industry, inflation, price index, indexes, price, and USA.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
vala khorasani (2024). Gym Members Exercise Dataset [Dataset]. https://www.kaggle.com/datasets/valakhorasani/gym-members-exercise-dataset
Organization logo

Gym Members Exercise Dataset

Analyzing Fitness Patterns and Performance Across Diverse Gym Experience Levels

Explore at:
14 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Oct 6, 2024
Dataset provided by
Kagglehttp://kaggle.com/
Authors
vala khorasani
License

Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically

Description

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.

Search
Clear search
Close search
Google apps
Main menu