Fitness Exercises Dataset

This dataset contains 1300+ samples of exercises with body parts, target muscles, secondary muscles and instructions. Each exercise has also a GIF url.

For the GIFs not loading:

I bought them for $1 each so that I can give them to you with 75% off! The FULL 1324 GIFs package for only the first 5 to grab them (ONLY 1 LEFT!) at that price from https://omarxadel.gumroad.com/l/exercisesdb

https://public-files.gumroad.com/7hxdwndfkmpyxb5r9k7g0yh07iqz" alt="">

✔ High Quality GIFs

✔ The whole thing (1324 GIFs)

✔ Sorted as they are in the database so you'll only need to run 1 job to change the names

✔ No watermarks

And they're limited. Make sure to get them before anyone else.😄

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7974466%2F69e65ffa17d835dc093fc520999e492c%2FLeonardo_Kino_XL_Create_a_vibrant_and_dynamic_digital_illustra_2.jpg?generation=1728979114108033&alt=media" alt="">

This dataset provides detailed information on 50 diverse exercises designed to promote overall health and fitness. It includes a wide range of activities suitable for beginners to advanced fitness enthusiasts, targeting various muscle groups and fitness goals. The data can be used for personal fitness planning, workout app development, or data analysis projects in health and sports science.

Column Descriptions

1. Name of Exercise: The common name of the exercise. Type: String Description: Unique identifier for each exercise in the dataset.

2. Sets: The recommended number of sets for the exercise. Type: Integer Description: Indicates how many times the group of repetitions should be performed.

3. Reps: The recommended number of repetitions per set. Type: Integer Description: Specifies how many times the exercise should be performed in each set.

4. Benefit: The primary health or fitness benefit of the exercise. Type: String Description: Briefly explains the main advantage or target of the exercise.

5. Burns Calories (per 30 min): Estimated calorie burn for a 30-minute session. Type: Integer Description: Approximates the number of calories burned by an average person (155 lbs/70 kg) performing the exercise for 30 minutes.

6. Target Muscle Group: The main muscles or muscle groups engaged during the exercise. Type: String Description: Lists the primary muscles worked, helping users target specific areas.

7. Equipment Needed: Any equipment required to perform the exercise. Type: String Description: Specifies necessary equipment, or "None" if the exercise can be performed without equipment.

8. Difficulty Level: The relative challenge level of the exercise. Type: String Description: Categorizes exercises as "Beginner," "Intermediate," or "Advanced" to guide appropriate selection based on fitness level.

It sounds like you have a substantial amount of personal exercise and health data accumulated over 150 days. This data can provide valuable insights into your fitness journey and overall well-being. Here are some suggestions on how you can analyze and make the most of this information:

Exercise Types:

Identify the types of exercises you've been engaging in. Categorize them into cardiovascular, strength training, flexibility, and other categories. Note the frequency and duration of each type of exercise.

Intensity Levels: Assess the intensity of your workouts. This can be measured in terms of heart rate, perceived exertion, or weight lifted. Determine if there are patterns in intensity levels over time.

Progress and Setbacks: Look for trends in your progress. Are you consistently improving, or have you encountered any setbacks? Identify factors that contribute to your success or challenges.

Rest and Recovery: Analyze your rest days and recovery strategies. Ensure that you're allowing your body enough time to recover between intense workouts. Look for patterns in your energy levels and performance related to rest.

Nutrition and Hydration: Correlate your exercise data with your nutrition and hydration habits. Consider whether certain eating patterns impact your workouts positively or negatively.

Sleep Patterns: Examine your sleep data if available. Adequate sleep is crucial for recovery and overall health. Identify any correlations between your sleep patterns and exercise performance.

Mood and Stress Levels: Reflect on your mood and stress levels on different days. Exercise can have a significant impact on mental well-being. Consider whether there are connections between your exercise routine and your emotional state.

Injury Analysis: If you've experienced any injuries during this period, analyze the circumstances surrounding them. This can help in understanding potential risk factors.

Goal Alignment: Evaluate whether your exercise routine aligns with your initial goals. Are you progressing toward your desired outcomes?

Adjustment of Exercise Routine: Based on the analysis, consider adjustments to your exercise routine. This might involve modifying the types of exercises, intensity, or frequency.

Remember, the goal of analyzing this data is to make informed decisions about your fitness routine, identify areas of improvement, and celebrate your successes. If you have specific questions about the data or need guidance on certain aspects, feel free to provide more details for personalized advice.

Google data search exercises can be used to practice finding data or statistics on a topic of interest, including using Google's own internal tools and by using advanced operators.

I needed a dataset of gym exercises, the muscles targeted by them, the equipment used and a brief description of each exercise for my project- however, I was unable to find a dataset like this anywhere- so I created one with data pulled from bodybuilding.com .

This dataset contains 470 gym exercises, links providing a description, the muscles targeted by them, the equipment used and a brief explanation of each equipment. Think of it as an all-you-need dataset either for any gym exercise related projects or for creating your workout program.

Happy Kaggling!

DORTROX/Exercises-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset is a comprehensive list of gym exercises that can be used to improve your fitness. It includes exercises for all levels of fitness, from beginners to advanced. The dataset also includes information on the muscles worked by each exercise, the equipment needed, and how to do the exercise safely.

This dataset can be used to create a personalized workout routine that meets your individual fitness goals. You can use the information in the dataset to choose exercises that target the muscles you want to strengthen or tone. You can also use the information to find exercises that are safe for your fitness level.

The dataset is a valuable resource for anyone who wants to improve their fitness. It can be used by beginners to learn the basics of gym exercises, by intermediate exercisers to find new and challenging exercises, and by advanced exercisers to fine-tune their workouts.

Here are some additional tips for using the dataset:

Start with a few exercises and gradually add more as you get stronger. Listen to your body and don't push yourself too hard. Warm up before you start your workout and cool down afterwards. Stay hydrated by drinking plenty of water. Eat a healthy diet to support your fitness goals.

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.

Location of Exercise Equipment within SDCC County. Point data identifying location and name included.

This Gym Exercise Dataset offers a comprehensive examination of various exercises and their detailed components. It focuses specifically on exercises performed using machines commonly available in gym settings.

The dataset encompasses: - Detailed breakdowns of machine-based exercises - Specific components and parameters for each exercise - Information on proper form and technique - Data on muscle groups targeted by each exercise

This collection serves as a valuable resource for: - Fitness professionals developing evidence-based training programs - Researchers studying exercise biomechanics and efficiency - Gym equipment manufacturers interested in user interaction data - Data scientists exploring patterns in exercise routines and preferences

The MEx Multi-modal Exercise dataset contains data of 7 different physiotherapy exercises, performed by 30 subjects recorded with 2 accelerometers, a pressure mat and a depth camera.

Application The dataset can be used for exercise recognition, exercise quality assessment and exercise counting, by developing algorithms for pre-processing, feature extraction, multi-modal sensor fusion, segmentation and classification.

** Data collection method ** Each subject was given a sheet of 7 exercises with instructions to perform the exercise at the beginning of the session. At the beginning of each exercise the researcher demonstrated the exercise to the subject, then the subject performed the exercise for maximum 60 seconds while being recorded with four sensors. During the recording, the researcher did not give any advice or kept count or time to enforce a rhythm.

** Sensors** Obbrec Astra Depth Camera - sampling frequency – 15Hz - frame size – 240x320

Sensing Tex Pressure Mat - sampling frequency – 15Hz - frame size – 32*16

Axivity AX3 3-Axis Logging Accelerometer - sampling frequency – 100Hz - range – 8g

** Sensor Placement** All the exercises were performed lying down on the mat while the subject wearing two accelerometers on the wrist and the thigh. The depth camera was placed above the subject facing down-words recording an aerial view. Top of the depth camera frame was aligned with the top of the pressure mat frame and the subject’s shoulders such that the face will not be included in the depth camera video.

** Data folder ** MEx folder has four folders, one for each sensor. Inside each sensor folder, 30 folders can be found, one for each subject. In each subject folder, 8 files can be found for each exercise with 2 files for exercise 4 as it is performed on two sides. (The user 22 will only have 7 files as they performed the exercise 4 on only one side.) One line in the data files correspond to one timestamped and sensory data.

Attribute Information The 4 columns in the act and acw files is organized as follows: 1 – timestamp 2 – x value 3 – y value 4 – z value Min value = -8 Max value = +8

The 513 columns in the pm file is organized as follows: 1 - timestamp 2-513 – pressure mat data frame (32x16) Min value – 0 Max value – 1

The 193 columns in the dc file is organized as follows: 1 - timestamp 2-193 – depth camera data frame (12x16) dc data frame is scaled down from 240x320 to 12x16 using the OpenCV resize algorithm Min value – 0 Max value – 1

This data is from a study estimating the UK population prevalence of eating in response to exercise, and exploring what factors are associated with eating in response to exercise. 1054 participants were recruited via Prolific. Data was collected via Qualtrics.

Section 1: Introduction

Brief overview of dataset contents:

Current database contains anonymised data collected during exercise testing services performed on male and female participants (cycling, rowing, kayaking and running) provided by the Human Performance Laboratory, School of Medicine, Trinity College Dublin, Dublin 2, Ireland.

835 graded incremental exercise test files (285 cycling, 266 rowing / kayaking, 284 running)

Description file with each row representing a test file - COLUMNS: file name (AXXX), sport (cycling, running, rowing or kayaking)

Anthropometric data of participants by sport (age, gender, height, body mass, BMI, skinfold thickness,% body fat, lean body mass and haematological data; namely, haemoglobin concentration (Hb), haematocrit (Hct), red blood cell (RBC) count and white blood cell (WBC) count )

Test data (HR, VO2 and lactate data) at rest and across a range of exercise intensities

Derived physiological indices quantifying each individual’s endurance profile

Following a request from athletes seeking assessment by phone or e-mail the test protocol, risks, benefits and test and medical requirements, were explained verbally or by return e-mail. Subsequently, an appointment for an exercise assessment was arranged following the regulatory reflection period (7 days). Following this regulatory period each participant’s verbal consent was obtained pre-test, for participants under 18 years of age parent / guardian consent was obtained in writing. Ethics approval was obtained from the Faculty of Health Sciences ethics committee and all testing procedures were performed in compliance with Declaration of Helsinki guidelines.

All consenting participants were required to attend the laboratory on one occasion in a rested, carbohydrate loaded and well-hydrated state, and for male participants’ clean shaven in the facial region. All participants underwent a pre-test medical examination, including assessment of resting blood pressure, pulmonary function testing and haematological (Coulter Counter Act Diff, Beckmann Coulter, CA,US) review performed by a qualified medical doctor prior to exercise testing. Any person presenting with any cardiac abnormalities, respiratory difficulties, symptoms of cold or influenza, musculoskeletal injury that could impair performance, diabetes, hypertension, metabolic disorders, or any other contra-indicatory symptoms were excluded. In addition, participants completed a medical questionnaire detailing training history, previous personal and family health abnormalities, recent illness or injury, menstrual status for female participants, as well as details of recent travel and current vaccination status, and current medications, supplements and allergies. Barefoot height in metre (Holtain, Crymych, UK), body mass (counter balanced scales) in kilogram (Seca, Hamburg, Germany) and skinfold thickness in millimetre using a Harpenden skinfold caliper (Bath International, West Sussex, UK) were recorded pre-exercise.

Section 2: Testing protocols

2.1: Cycling

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on an electromagnetically braked cycle ergometer (Lode Excalibur Sport, Groningen, The Netherlands). Participants initially identified a cycling position in which they were most comfortable by adjusting saddle height, saddle fore-aft position relative to the crank axis, saddle to handlebar distance and handlebar height. Participant’s feet were secured to the ergometer using their own cycling shoes with cleats and accompanying pedals. The protocol commenced with a 15-min warm-up at a workload of 120 Watt (W), followed by a 10-min rest. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a workload of 100 or 120 W for female and male participants, respectively, and subsequently increasing by a 20, 30 or 40 W incremental increase every 3-min depending on gender and current competition category. During assessment participants maintained a constant self-selected cadence chosen during their warm-up (permitted window was 5 rev.min−1 within a permitted absolute range of 75 to 95 rev.min−1) and the test was terminated when a participant was no longer able to maintain a constant cadence.

Heart rate (HR) data were recorded continuously by radio-telemetry using a Cosmed HR monitor (Cosmed, Rome, Italy). During the test, blood samples were collected from the middle finger of the right hand at the end of the second minute of each 3-min interval. The fingertip was cleaned to remove any sweat or blood and lanced using a long point sterile lancet (Braun, Melsungen, Germany). The blood sample was collected into a heparinised capillary tube (Brand, Wertheim, Germany) by holding the tube horizontal to the droplet and allowing transfer by capillary action. Subsequently, a 25μL aliquot of whole blood was drawn from the capillary tube using a YSI syringepet (YSI, OH, USA) and added into the chamber of a YSI 1500 Sport lactate analyser (YSI, OH, USA) for determination of non-lysed [Lac] in mmol.L−1. The lactate analyser was calibrated to the manufacturer’s requirements (± 0.05 mmol.L−1) before each test using a standard solution (YSI, OH, USA) of known concentration (5 mmol.L−1) and analyser linearity was confirmed using either a 15 or 30 mmol.L-1 standard solution (YSI, OH, USA).

Gas exchange variables including respiration rate (Rf in breaths.min-1), minute ventilation (VE in L.min-1), oxygen consumption (VO2 in L.min-1 and in mL.kg-1.min-1) and carbon dioxide production (VCO2 in L.min-1), were measured on a breath-by-breath basis throughout the test, using a cardiopulmonary exercise testing unit (CPET) and an associated software package (Cosmed, Rome, Italy). Participants wore a face mask (Hans Rudolf, KA, USA) which was connected to the CPET unit. The metabolic unit was calibrated prior to each test using ambient air and an alpha certified gas mixture containing 16% O2, 5% CO2 and 79% N2 (Cosmed, Rome, Italy). Volume calibration was performed using a 3L gas calibration syringe (Cosmed, Rome, Italy). Barometric pressure recorded by the CPET was confirmed by recording barometric pressure using a laboratory grade barometer.

Following testing mean HR and mean VO2 data at rest and during each exercise increment were computed and tabulated over the final minute of each 3-min interval. A graphical plot of [Lac], mean VO2 and mean HR versus cycling workload was constructed and analysed to quantify physiological endurance indices, see Data Analysis section. Data for VO2 peak in L.min-1 (absolute) and in mL.kg-1.min-1 (relative) and VE peak in L.min-1 were reported as the peak data recorded over any 10 consecutive breaths recorded during the last minute of the final exercise increment.

2.2: Running protocol

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a motorised treadmill (Powerjog, Birmingham, UK). The running protocol, performed at a gradient of 0%, commenced with a 15-min warm-up at a velocity (km.h-1) which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity. Subsequently, the warm-up was followed by a 10 minute rest / dynamic stretching phase. From a safety perspective during all running GxT participants wore a suspended lightweight safety harness to minimise any potential falls risk. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal running velocity which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity, and subsequently increased by ≥ 1 km.h-1 every 3-min depending on gender and current competition category. The test was terminated when a participant was no longer able to maintain the imposed treadmill.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

2.3: Rowing / kayaking protocol

A discontinuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a Concept 2C rowing ergometer (Concept, VA, US) in rowers or a Dansprint kayak ergometer (Dansprint, Hvidovre, Denmark) in flat-water kayakers. The protocol commenced with a 15-min low-intensity warm-up at a workload (W) dependent on gender, sport and competition category, followed by a 10-min rest. For rowing the flywheel damping (120, 125 or 130W) was set dependent on gender and competition category. For kayaking the bungee cord tension was adjusted by individual participants to suit their requirements. A discontinuous protocol of 3-min exercise at a targeted load followed by a 1-min rest phase to facilitate stationary earlobe capillary blood sample collection and resetting of ergometer display (Dansprint ergometer) was used. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal load 80 to 120 W for rowing, 50 to 90 W for kayaking and subsequently increased by 20,30 or 40 W every 3-min depending on gender, sport and current competition category. The test was terminated when a participant was no longer able to maintain the targeted workload.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

3.1: Data analysis

Constructed graphical plots (HR, VO2 and [Lac] versus load / velocity) were analysed to quantify the following; load / velocity at TLac, HR at TLac, [Lac] at TLac, % of VO2 peak at TLac, % of HRmax at TLac, load / velocity and HR at a nominal [Lac] of 2 mmol.L-1, load / velocity, VO2 and [Lac} at a nominal HR of

This record contains the data files used in exercises in the NBIS course "Introduction to Data Management Practices".

The PHYTMO database contains data from physical therapy exercises and gait variations recorded with magneto-inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations commonly prescribed in physical therapies were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. Four magneto-inertial sensors were placed on the lower-or upper-limbs for the recording of the motions together with passive optical reflectors. The files include the specifications of the inertial sensors and the cameras. The database includes magneto-inertial data (linear acceleration, turn rate and magnetic field), together with a highly accurate location and orientation in the 3D space provided by the optical system (errors are lower than 1mm). The database files were stored in CSV format to ensure usability with common data processing software. The main aim of this dataset is the availability of inertial data for two main purposes: the analysis of different techniques for the identification and evaluation of exercises monitored with inertial wearable sensors and the validation of inertial sensor-based algorithms for human motion monitoring that obtains segments orientation in the 3D space. Furthermore, the database stores enough data to train and evaluate Machine Learning-based algorithms. The age range of the participants can be useful for establishing age-based metrics for the exercises evaluation or the study of differences in motions between different aged groups. Finally, the MATLAB function features_extraction, developed by the authors, is also given. This function splits signals using a sliding window, returning its segments, and extract signal features, in the time and frequency domains, based on prior studies of the literature.

The MEx Multi-modal Exercise dataset contains data of 7 different physiotherapy exercises, performed by 30 subjects recorded with 2 accelerometers, a pressure mat and a depth camera.

Application The dataset can be used for exercise recognition, exercise quality assessment and exercise counting, by developing algorithms for pre-processing, feature extraction, multi-modal sensor fusion, segmentation and classification.

** Data collection method ** Each subject was given a sheet of 7 exercises with instructions to perform the exercise at the beginning of the session. At the beginning of each exercise the researcher demonstrated the exercise to the subject, then the subject performed the exercise for maximum 60 seconds while being recorded with four sensors. During the recording, the researcher did not give any advice or kept count or time to enforce a rhythm.

** Sensors** Obbrec Astra Depth Camera - sampling frequency – 15Hz - frame size – 240x320

Sensing Tex Pressure Mat - sampling frequency – 15Hz - frame size – 32*16

Axivity AX3 3-Axis Logging Accelerometer - sampling frequency – 100Hz - range – 8g

** Sensor Placement** All the exercises were performed lying down on the mat while the subject wearing two accelerometers on the wrist and the thigh. The depth camera was placed above the subject facing down-words recording an aerial view. Top of the depth camera frame was aligned with the top of the pressure mat frame and the subject’s shoulders such that the face will not be included in the depth camera video.

** Data folder ** MEx folder has four folders, one for each sensor. Inside each sensor folder, 30 folders can be found, one for each subject. In each subject folder, 8 files can be found for each exercise with 2 files for exercise 4 as it is performed on two sides. (The user 22 will only have 7 files as they performed the exercise 4 on only one side.) One line in the data files correspond to one timestamped and sensory data.

Attribute Information The 4 columns in the act and acw files is organized as follows: 1 – timestamp 2 – x value 3 – y value 4 – z value Min value = -8 Max value = +8

The 513 columns in the pm file is organized as follows: 1 - timestamp 2-513 – pressure mat data frame (32x16) Min value – 0 Max value – 1

The 193 columns in the dc file is organized as follows: 1 - timestamp 2-193 – depth camera data frame (12x16) dc data frame is scaled down from 240x320 to 12x16 using the OpenCV resize algorithm Min value – 0 Max value – 1

Zip file with 5 Roam exercises in PDF and PPTX formats, plus trainer guide and Quick Guide. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-04-10 and migrated to Edinburgh DataShare on 2017-02-22.

Objective To compare de cognitive development of the seniors that practice and the one that doesn’t practice physical exercises. Methods This transversal study was made with 104 senior citizens, 64 belonging to the physical exercises Practicing Group (G1) and 40 belonging to the Non Practicing Group (G2), registered on Health Centers. It was a applied a Mini Exam of Mental Health (MEEM) to assess the cognitive health and a record for the characterize the sample. Afterwards, it was applied a Battery of Cognitive Computerized Evaluation (CogState) to assess the cognitive development of the senior citizens. There was utilized the U Mann Whitney test to compare the groups and the computation of the measure of effect d of Cohen, to verify if the practice of physical exercise influence on the cognitive development. To the descriptive assessment there was utilized data expressed in average, standard deviation, median and percentage. There was accepted the level of significance of 5%. Results The score on the MEEM have presented statistically significant differences between the groups. About the cognitive development, mensured by CogState, the groups diverged significantly to all the analyzed variables, presenting the G1 the best performance on the time tests of time and simple reaction, of choice and assisted attention; on the other hand, the G2 had better performance on the tests of short time memory and work. Conclusions Senior citizens practicing physical exercises have better performance for simple reaction time, choice reaction time and assisted care, when compared to older non-practicing.

Matlab codes for the solution of exercises which can be found in the book "Tensor Calculus and Differential Geometry for Engineers"

Related publications:

Shahab Sahraee, Peter Wriggers: Tensor Calculus and Differential Geometry for Engineers, Springer, Berlin, to be published.

The aim of this study was to analyze the trunk muscular response during different variations of some of the most popular stabilization exercises: front-bridge, back-bridge, side-bridge, and bird-dog. Surface electromyography was bilaterally recorded from rectus abdominis, external and internal oblique and erector spinae during 25 variations of the aforementioned exercises. Compared to the conventional form of the front- and side-bridge, performing these exercises kneeling on a bench or with elbows extended reduced the muscular challenge. Conversely, performing the back-bridge with elbows extended elicited higher muscular activation than the conventional exercise. While bridge exercises with double leg support produced the highest activation levels in those muscles that counteracted gravity, single leg support while bridging increased the activation of the trunk rotators, especially internal oblique. The highest activation levels were found in three exercises: sagittal walkout in a front-bridge position, rolling from right side-bridge into front-bridge position, and side-bridge with single leg support on a BOSUTMbalance trainer. Although the exercises performed on unstable surfaces usually enhanced the muscle activation, performing the exercises on the BOSUTMbalance trainer did not always increase the trunk muscle activity. Overall, this information may be useful to guide fitness instructors and clinicians when establishing stabilization exercise progressions for the trunk musculature.