| | --- |
| | license: mit |
| | --- |
| | # AI Skating Coach - Figure Skating Element Recognition Dataset |
| |
|
| | **Clean 64-class version with multi-jump combinations preserved** |
| |
|
| | ## Overview |
| |
|
| | Figure skating skeleton pose sequences for action/element classification. Raw keypoint data extracted from competition videos and professional motion capture, presented in clean unmodified form. |
| |
|
| | - **Total samples:** 5,405 |
| | - **Training:** 4,324 sequences |
| | - **Test:** 1,081 sequences |
| | - **Classes:** 64 figure skating elements |
| | - **Format:** Clean unaugmented data (no synthetic samples, no class weights) |
| |
|
| | ## Dataset Structure |
| |
|
| | ``` |
| | ├── train_data.pkl # Training sequences (4,324) |
| | ├── train_label.pkl # Training labels |
| | ├── test_data.pkl # Test sequences (1,081) |
| | ├── test_label.pkl # Test labels |
| | ├── label_mapping.json # Class IDs and names |
| | └── dataset_info.json # Metadata |
| | ``` |
| |
|
| | ## Data Format |
| |
|
| | **Skeleton sequences:** `(num_samples, variable_frames, 17_keypoints, 3_coordinates)` |
| |
|
| | - **Frames:** Variable length from original footage (original temporal resolution preserved) |
| | - **Duration:** Varies by element (typically 2-25 seconds at 30 fps) |
| | - **Keypoints:** 17-point COCO format |
| | - Head: nose, left/right eye, left/right ear |
| | - Torso: shoulders, elbows, wrists, hips, knees, ankles |
| | - **Coordinates:** (x, y, confidence) normalized to [-1, 1] range |
| |
|
| | ## Classes (64 Total) |
| |
|
| | ### Single Jump Elements (0-20) |
| | Single rotation jumps: Axel, Flip, Lutz, Loop, Salchow, Toeloop |
| | Rotations: 1x, 2x, 3x, 4x (where applicable) |
| |
|
| | **Examples:** 1Axel, 2Flip, 3Lutz, 4Toeloop |
| |
|
| | ### Multi-Jump Combinations (21-30) |
| | Natural sequence patterns from competition: |
| | - 1A+3T, 1A+3A |
| | - 2A+3T, 2A+3A, 2A+1Eu+3S |
| | - 3F+3T, 3F+2T+2Lo |
| | - 3Lz+3T, 3Lz+3Lo |
| | - Generic Combination (Comb) |
| |
|
| | ### Spins (31-62) |
| | Rotational elements with position changes: |
| | - **FCSp** (Foot Change Camel Spin): 31-34 |
| | - **CCoSp** (Catch Foot Combination Spin): 35-38 |
| | - **ChCamelSp** (Change Camel Spin): 39-42 |
| | - **ChComboSp** (Change Combination Spin): 43-46 |
| | - **ChSitSp** (Change Sit Spin): 47-50 |
| | - **FlySitSp** (Fly Sit Spin): 51-54 |
| | - **LaybackSp** (Layback Spin): 55-58 |
| |
|
| | ### Step Sequences & Choreography (59-63) |
| | Linear traveling skating patterns: |
| | - **StepSeq1-4:** Graded step sequences (59-62) |
| | - **ChoreSeq1:** Choreographed sequence (63) |
| |
|
| | ## Data Sources |
| |
|
| | 1. **MMFS Dataset** (4,915 sequences) |
| | - 2D pose estimation from figure skating competition videos |
| | - Multiple skaters, various competition levels |
| |
|
| | 2. **JSON Motion Capture** (253 sequences) |
| | - Professional 3D mocap capture from 4 elite skaters |
| | - Converted to 17-keypoint COCO format for consistency |
| |
|
| | 3. **Combined & Validated** (5,405 sequences) |
| | - Merged MMFS and mocap data |
| | - Deduplicated overlapping classes |
| | - Combinations preserved for sequence modeling |
| |
|
| | ## Preprocessing |
| | **Format unification:** 142-marker mocap → 17-keypoint COCO skeleton |
| | **Temporal sampling:** Uniform to 150 frames per sequence |
| | **Normalization:** Keypoint coordinates normalized to [-1, 1] |
| | **Velocity features:** Computed for temporal dynamics |
| | **Train/test split:** 80/20 stratified by class |
| |
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|
| | - |
| |
|
| | ## Loading the Dataset |
| |
|
| | ### Python |
| | ```python |
| | import pickle |
| | import json |
| | import numpy as np |
| | |
| | # Load training sequences and labels |
| | with open('train_data.pkl', 'rb') as f: |
| | X_train = pickle.load(f) # List of (150, 17, 3) arrays |
| | with open('train_label.pkl', 'rb') as f: |
| | y_train = pickle.load(f) # Array of class IDs (0-63) |
| | |
| | # Load test data |
| | with open('test_data.pkl', 'rb') as f: |
| | X_test = pickle.load(f) |
| | with open('test_label.pkl', 'rb') as f: |
| | y_test = pickle.load(f) |
| | |
| | # Load class mapping |
| | with open('label_mapping.json', 'r') as f: |
| | mapping = json.load(f) |
| | |
| | # Inspect |
| | print(f"Training: {len(X_train)} sequences, {X_train[0].shape}") |
| | print(f"Classes: {len(np.unique(y_train))}") |
| | print(f"Class weights: {np.bincount(y_train)}") # Raw distribution |
| | ``` |
| |
|
| | ### Convert to NumPy |
| | ```python |
| | import numpy as np |
| | |
| | # Stack sequences into array |
| | X_train_array = np.array(X_train) # (4324, 150, 17, 3) |
| | X_test_array = np.array(X_test) # (1081, 150, 17, 3) |
| | ``` |
| |
|
| | ## Recommended Usage |
| |
|
| | ### Action Recognition |
| | - CNN-LSTM architecture for 64-class classification |
| | - Input: (batch, 150, 17, 3) sequences |
| | - Output: 64-class softmax |
| |
|
| | ### Sequence Modeling |
| | - Use combinations (classes 21-30) for multi-step skill prediction |
| | - Temporal modeling with RNNs/Transformers |
| | - Learn natural skill progression patterns |
| |
|
| | ### Transfer Learning |
| | 1. Pretrain on combinations for sequence context |
| | 2. Fine-tune on single jumps for element detection |
| | 3. Apply to event/routine-level classification |
| |
|
| | ### Sports Analytics |
| | - Skill difficulty assessment |
| | - Athlete performance tracking |
| | - Technique consistency analysis |
| |
|
| | ## Class Distribution |
| |
|
| | For detailed per-class sample counts, see `dataset_info.json` |
| |
|
| | **Imbalance ratio:** ~6x (largest/smallest class) |
| | **Skew:** Toward more common elements (2-3 rotations, standard spins) |
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|
| | Dataset compiled from public figure skating competition videos and proprietary motion capture data. Use for research and educational purposes. |
| |
|
| | --- |
| |
|
| | **Generated:** February 2026 |
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