Update README.md

README.md

@@ -1,3 +1,174 @@
----
-license: mit
----
+---
+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  
+