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.

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.

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.

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.

Dataset

This dataset was created by Nikolas Luiz Schmitt

Released under Apache 2.0

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Dataset

This dataset was created by VARAHALARAJU

Released under MIT

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🏋️ About This Dataset This comprehensive fitness dataset contains over 600,000 structured workout routines and exercise entries scraped from fitness planning platform data. The dataset includes both detailed exercise-level data and program-level summaries, making it ideal for building recommendation systems, analyzing workout patterns, and understanding fitness program structures.

📊 Dataset Overview Two complementary files: 1. Main Dataset (fitness_exercises.csv): 605,033 individual exercise entries with detailed workout information 2. Program Summary (program_summary.csv): 2,598 unique fitness programs with aggregated metadata

🔑 Key Features

Main Dataset (605K+ rows): - Exercise details: name, sets, reps, intensity - Program structure: week, day, time per workout - User targeting: fitness level, goals, equipment needs - Temporal data: creation and edit timestamps - Program metadata: length, number of exercises per workout

Program Summary (2.6K+ programs): - Program overview: title, description, fitness level - Target goals and equipment requirements - Program duration and workout timing - Total exercise count per program - Creation and modification timestamps

🎯 Use Cases - Building workout recommendation systems - Analyzing fitness program effectiveness and popularity - Understanding exercise patterns and program structures - Creating personalized workout generators - Fitness app development and research - Program-level analysis and clustering

🔧 Technical Details - Format: CSV files - Combined size: ~300MB+ - Data quality: Minimal missing values (<1% for most columns) - Collection period: [Add your scraping date] - Source: Fitness platform data (with attribution)

📝 Data Dictionary

Main Dataset: - title: Workout/program name - description: Detailed workout description - level: Fitness level (beginner/intermediate/advanced) - goal: Primary fitness objective - equipment: Required equipment type - program_length: Duration in weeks - time_per_workout: Duration per session (minutes) - week/day: Position in program structure - exercise_name: Specific exercise name - sets/reps: Exercise volume parameters (negative values are time in seconds) - intensity: Exercise intensity level

Program Summary: - title: Program name - description: Program overview and objectives - level: Target fitness level - goal: Primary fitness goal - equipment: Required equipment - program_length: Total program duration (weeks) - time_per_workout: Average workout duration (minutes) - total_exercises: Total number of exercises in program - created/last_edit: Program timestamps

🔗 Data Relationship The program_summary file provides aggregated views of the detailed exercise data, allowing for both micro-level exercise analysis and macro-level program insights.

⚖️ Important Notes - Data collected from publicly available fitness planning platform - Cleaned and structured for research/educational use - Please respect original platform's terms of service - Consider this for non-commercial research and educational purposes

Description:

This dataset contains the number of calories burned by a person while performing some activity/exercise. It currently contains 248 activities and exercises ranging from running, cycling, calisthenics, etc.

Getting started:

Check the discussion boards or kernels on how to get started with the dataset.

Inspiration:

I had been searching for a similar dataset containing the number of calories burned mapped with the exercise names, but couldn't find one. So I compiled this dataset manually!

DEMO OF MY PROJECT THAT USED THIS DATASET (AI PERSONAL TRAINER):

GITHUB PROJECT: https://github.com/RiccardoRiccio/Fitness-AI-Trainer-With-Automatic-Exercise-Recognition-and-Counting

DESCRIPTION OF THE DATASET

This dataset was created for real-time fitness exercise classification and includes a diverse mix of synthetic and real-world videos. It focuses on four common exercises:

• Squat
• Push-up
• Barbell Bicep Curl
• Shoulder Press

The dataset was compiled from three main sources:

📁 Kaggle Workout/Exercises Video Dataset

• Real-world videos of expert trainers performing various exercises
• Only four exercises were selected
• ~25 videos per class were curated, ensuring balanced representation
• Supplemented with additional online videos to increase variation in lighting, angle, and environment

🧍 InfiniteRep Dataset

• Synthetic videos of human-like avatars performing exercises
• 100 videos per class selected
• Offers control over pose variation, camera angles etc.
• Enhances model robustness and dataset size

🌐 Additional Online Videos

• Sourced from Pexels, Pixabay, Shutterstock, etc.
• Added to reflect how users might perform exercises in home or gym environments

The Physical Action Data Set includes 10 normal and 10 aggressive physical actions that measure the human activity. This dataset consists of EMG signals recorded from 8 total muscles; the biceps and triceps of both the arms and the hamstrings and thigh muscles of both legs.The data have been collected by 4 subjects using the Delsys EMG wireless apparatus.

Additional Information

Protocol: Three male and one female subjects (age 25 to 30), who have experienced aggression in scenarios such as physical fighting, took part in the experiment. Throughout 20 individual experiments, each subject had to perform ten normal and ten aggressive activities. Regarding the rights of the subjects involved, ethical regulations and safety precaution have been followed based on the code of ethics of the British psychological society. The regulations explain the ethical legislations to be applied when experiments with human subjects are conducted. According to the experimental setup and the precautions taken, the ultimate risk of injuries was minimal. The subjects were aware that since their involvement in this series of experiments was voluntary, it was made clear that they could withdraw at any time from the study.

Instrumentation: The Essex robotic arena was the main experimental hall where the data collection took place. With area 4x5.5m, the subjects expressed aggressive physical activities at random locations. A professional kick-boxing standing bag has been used, 1.75m tall, with a human figure drawn on its body. The subjects’ performance has been recorded by the Delsys EMG apparatus, interfacing human activity with myoelectrical contractions. Based on this context, the data acquisition process involved eight skin-surface electrodes placed on the upper arms (biceps and triceps), and upper legs (thighs and hamstrings).

Data Setup: The overall number of electrodes is 8, which corresponds to 8 input time series one for a muscle channel (ch1-8). Each time series contains ~10000 samples (~15 actions per experimental session for each subject). More: readme.txt

Dataset

This dataset was created by Mesut Yurukcu

Released under Database: Open Database, Contents: Database Contents

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Dataset

This dataset was created by Serkan Polat

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Dataset

This dataset was created by taqwa km

Released under MIT

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This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Individual reports can be parsed by export session ID (column A) or timestamp (column B). Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors / preferences.

Dataset

This dataset was created by Mohammadreza Aliyari

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Dataset

This dataset was created by Jeff Rajeck

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Dataset

This dataset was created by Sarvajit Kumar

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Dataset

This dataset was created by Donny Le

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Dataset

This dataset was created by ZhangWeiXuan

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Dataset

This dataset was created by Youssef Bedeer

Released under Apache 2.0

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