| --- |
| 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 |
|
|
| 1. **Raw Frame Acquisition:** 920 high-quality frames across diverse subjects performing physiotherapy core exercises were curated. |
| 2. **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)* |
| 3. **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 |
|
|
| ```python |
| 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 |
|
|
| ```python |
| 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](https://www.apache.org/licenses/LICENSE-2.0). |
|
|
