Datasets:

xgboostgod
/

CoreExercise5K

license: apache-2.0
task_categories:
  - tabular-classification
tags:
  - pose-estimation
  - mediapipe
  - core-exercises
  - physical-therapy
  - xgboost
  - biomechanics
  - rehabilitation
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data.csv

CoreExercise5K: A Machine Learning Dataset for Core Exercise Assessment

CoreExercise5K is a tabular dataset composed of 3D skeletal landmark coordinates and joint angles extracted using MediaPipe Pose. It was curated for machine-learning-based physical therapy and real-time core exercise posture auditing.

📌 Dataset Overview

Total Samples: 5,016 rows
Number of Features: 108 columns (33 MediaPipe pose landmarks $\times$ $x, y, z$ coordinates + derived joint angle attributes)
Target Variable (label): 9 classes (8 core exercise movements + neutral/none)
Modality: Tabular / Skeletal Landmarks (Normalized 3D coordinates)

🏋️‍♂️ Class Distribution

Exercise Class	Sample Count	Percentage
Bird Dog	965	19.24%
Plank	830	16.55%
None (Neutral / Inactive)	622	12.40%
High Plank	579	11.54%
Dead Bug	492	9.81%
Glute Bridge	479	9.55%
Side Plank	416	8.29%
Dart	357	7.12%
Knee Plank	276	5.50%
Total	5,016	100%

🔬 Data Collection & Preprocessing

Raw Frame Acquisition: 920 high-quality frames across diverse subjects performing physiotherapy core exercises were curated.
Data Augmentation: An augmentation pipeline applied 6 transformations to each frame:
- Original frame
- $+15^\circ$ rotation
- $-15^\circ$ rotation
- Horizontal flip
- Flipped with $+15^\circ$ rotation
- Flipped with $-15^\circ$ rotation
  (Yielding a theoretical maximum of $920 \times 6 = 5,520$ frames)
Landmark Detection & Quality Filtering: Each augmented frame was passed through the MediaPipe Pose model. Frames with occlusions or missing keypoints where MediaPipe failed detection were filtered out, resulting in 5,016 clean, fully labeled keypoint vectors.

💻 Getting Started

You can load this dataset easily with the Hugging Face datasets library or directly with pandas:

Using Hugging Face Datasets

from datasets import load_dataset

# Load dataset
dataset = load_dataset("xgboostgod/CoreExercise5K")
df = dataset["train"].to_pandas()

print(f"Dataset shape: {df.shape}")
print(df["label"].value_counts())

Using Pandas Directly

import pandas as pd

url = "https://huggingface.co/datasets/xgboostgod/CoreExercise5K/resolve/main/data.csv"
df = pd.read_csv(url)
print(df.head())

📜 License

This dataset is released under the Apache 2.0 License.