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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
-
## 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)
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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