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">

onurSakar/GYM-Exercise dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

2 accelerometers

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

A January 2022 survey worldwide revealed that a majority of respondents did exercise from the comfort of their own homes. By contrast, just under ** percent of respondents stated that they did not take part in home workouts.

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.

In this paper, we introduce a novel benchmarking framework designed specifically for evaluations of data science agents. Our contributions are three-fold. First, we propose DSEval, an evaluation paradigm that enlarges the evaluation scope to the full lifecycle of LLM-based data science agents. We also cover aspects including but not limited to the quality of the derived analytical solutions or machine learning models, as well as potential side effects such as unintentional changes to the original data. Second, we incorporate a novel bootstrapped annotation process letting LLM themselves generate and annotate the benchmarks with ``human in the loop''. A novel language (i.e., DSEAL) has been proposed and the derived four benchmarks have significantly improved the benchmark scalability and coverage, with largely reduced human labor. Third, based on DSEval and the four benchmarks, we conduct a comprehensive evaluation of various data science agents from different aspects. Our findings reveal the common challenges and limitations of the current works, providing useful insights and shedding light on future research on LLM-based data science agents.

This is one of DSEval benchmarks.

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

Dataset Features

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

Distribution

https://storage.googleapis.com/opendatabay_public/b4edb3d3-3b74-4695-bd99-64e0e4751b52/4caa9c282175_gym1.png" alt="Synthetic Gym Members Exercise Data Distribution">

Usage

This dataset is suited for the following applications:

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

License

CC0 (Public Domain)

Who Can Use It

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

The data presented here was extracted from a larger dataset collected through a collaboration between the Embedded Systems Laboratory (ESL) of the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland and the Institute of Sports Sciences of the University of Lausanne (ISSUL). In this dataset, we report the extracted segments used for an analysis of R peak detection algorithms during high intensity exercise.

Protocol of the experiments
The protocol of the experiment was the following.

• 22 subjects performing a cardio-pulmonary maximal exercise test on a cycle ergometer, using a gas mask. A single-lead electrocardiogram (ECG) was measured using the BIOPAC system.
• An initial 3 min of rest were recorded.
• After this baseline, the subjects started cycling at a power of 60W or 90W depending on their fitness level.
• Then, the power of the cycle ergometer was increased by 30W every 3 min till exhaustion (in terms of maximum oxygen uptake or VO2max).
• Finally, physiology experts assessed the so-called ventilatory thresholds and the VO2max based on the pulmonary data (volume of oxygen and CO2).

Description of the extracted dataset

The characteristics of the dataset are the following:

• We report only 20 out of 22 subjects that were used for the analysis, because for two subjects the signals were too corrupted or not complete. Specifically, subjects 5 and 12 were discarded.
• The ECG signal was sampled at 500 Hz and then downsampled at 250 Hz. The original ECG signal were measured at maximum 10 mV. Then, they were scaled down by a factor of 1000, hence the data is represented in uV.
• For each subject, 5 segments of 20 s were extracted from the ECG recordings and chosen based on different phases of the maximal exercise test (i.e., before and after the so-called second ventilatory threshold or VT2, before and in the middle of VO2max, and during the recovery after exhaustion) to represent different intensities of physical activity.

seg1 --> [VT2-50,VT2-30]
seg2 --> [VT2+60,VT2+80]
seg3 --> [VO2max-50,VO2max-30]
seg4 --> [VO2max-10,VO2max+10]
seg5 --> [VO2max+60,VO2max+80]

• The R peak locations were manually annotated in all segments and reviewed by a physician of the Lausanne University Hospital, CHUV. Only segment 5 of subject 9 could not be annotated since there was a problem with the input signal. So, the total number of segments extracted were 20 * 5 - 1 = 99.

Format of the extracted dataset

The dataset is divided in two main folders:

• The folder `ecg_segments/` contains the ECG signals saved in two formats, `.csv` and `.mat`. This folder includes both raw (`ecg_raw`) and processed (`ecg`) signals. The processing consists of a morphological filtering and a relative energy non filtering method to enhance the R peaks. The `.csv` files contain only the signal, while the `.mat` files include the signal, the time vector within the maximal stress test, the sampling frequency and the unit of the signal amplitude (uV, as we mentioned before).
• The folder `manual_annotations/` contains the sample indices of the annotated R peaks in `.csv` format. The annotation was done on the processed signals.

This original dataset contains physiological signals collected during structured acute stress induction and aerobic and anaerobic exercise sessions using a wearable device. Blood volume pulse, motion-based activity, skin temperature, and electrodermal activity were recorded with the Empatica E4, a research-grade wearable. The stress induction protocol involved math and emotional tasks designed to provoke stress responses, interleaved with rest periods. Self-reported stress levels were also recorded during this procedure. For the exercise sessions, distinct routines on a stationary bike were created for aerobic and anaerobic activities. The dataset includes records from 36 healthy volunteers for stress sessions, 30 for aerobic exercise, and 31 for anaerobic exercise. By examining the variations in physiological signals, the effects of these activities can be analyzed. This dataset is a valuable resource for research on stress and exercise detection and classification.

Includes the raw IMU data for 20 participants performing seven different running exercises each.

20 participants: 16 m, 4 f, 16 to 31 yo, healthy, do sports regularly Seven exercises: Carioca left, carioca right, heel-to-butt, high-knee running, sideskips left, sideskips right, regular running Four IMUs: accelerometer + gyroscope each, two at wrists, two at ankles Ten seconds per recording under supervision One .json file per recording with sensor values and timestamps

Exercise

## Overview

Exercise is a dataset for computer vision tasks - it contains Exercise annotations for 312 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The dataset contains short video clips of four shoulder exercises.Arm flexion and extensionArm abduction and adductionArm lateral and medial rotationArm circumduction The videos are labeled as either correct or incorrect.

The share of respondents in the United States taking part in aerobic or cardio workouts dropped by ** percent between 2019 and 2024. Meanwhile, just over ** percent of respondents in 2024 stated that they took part in weight training or lifted weights.

Dataset

This dataset was created by Talha Anjum

Released under Apache 2.0

Contents

A September 2023 survey on exercise habits in the United States revealed that around ** percent of respondents used wearable fitness trackers to track their workouts. Moreover, ** percent of respondents used online training platforms to enhance their exercise routines.

This data set is an example data set for the data set used in the experiment of the paper "A Multilevel Analysis and Hybrid Forecasting Algorithm for Long Short-term Step Data". It contains two parts of hourly step data and daily step data

Dataset from the paper Gonzalez-Hernandez, F., Etnier, J., Zabala, M., & Sanabria, D. (2017). Vigilance Performance during acute exercise. International Journal of Sport Psychology, doi: 10.7352/IJSP 2017.48.000

Diabetes, a chronic metabolic condition characterised by persistently high blood sugar levels, necessitates early detection to mitigate its risks. Inadequate dietary choices can contribute to various health complications, emphasising the importance of personalised nutrition interventions. However, real-time selection of diets tailored to individual nutritional needs is challenging because of the intricate nature of foods and the abundance of dietary sources. Because diabetes is a chronic condition, patients with this illness must choose a healthy diet. Patients with diabetes frequently need to visit their doctor and rely on expensive medications to manage their condition. It is challenging to purchase medication for chronic illnesses on a regular basis in underdeveloped nations. Motivated by this concept, we suggest a hybrid model that, rather than depending solely on medication to evade a visit to the doctor, can first anticipate diabetes and then suggest a diet and exercise regimen. This research proposes an optimized approach by harnessing machine learning classifiers, including Random Forest, Support Vector Machine, and XGBoost, to develop a robust framework for accurate diabetes prediction. The study addresses the difficulties in predicting diabetes precisely from limited labeled data and outliers in diabetes datasets. Furthermore, a thorough food and exercise recommender system is unveiled, offering individualized and health-conscious nutrition recommendations based on user preferences and medical information. Leveraging efficient learning and inference techniques, the study achieves a meager error rate of less than 30% using an extensive dataset comprising over 100 million user-rated foods. This research underscores the significance of integrating machine learning classifiers with personalized nutritional recommendations to enhance diabetes prediction and management. The proposed framework has substantial potential to facilitate early detection, provide tailored dietary guidance, and alleviate the economic burden associated with diabetes-related healthcare expenses.