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b94b2ad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | # Classification Problems Definition
This directory contains prepared data for 2 classification problems with 2 approaches each, derived from the processed skeleton data.
## Problem Definitions
### Problem A: 3D Classification (Kinect-based)
- **Input**: 3D joint coordinates from Kinect sensor
- **Structure**: 13 joints × 3 dimensions (x, y, z) = 39 features per frame
- **Temporal**: 10 frames per sequence
- **Total features**: 39 × 10 = 390 features per sequence
- **Task**: Classify movement quality as Good (1) or Bad (0)
#### Approaches:
- **ADense**: Flattened features for dense neural networks
- Shape: (samples, 390)
- **ACNN**: Structured features for convolutional neural networks
- Shape: (samples, 10, 13, 3) - [time_steps, joints, coordinates]
### Problem B: 2D Classification (PoseNet-based)
- **Input**: 2D joint coordinates from PoseNet/MediaPipe
- **Structure**: 13 joints × 2 dimensions (x, y) = 26 features per frame
- **Temporal**: 10 frames per sequence
- **Total features**: 26 × 10 = 260 features per sequence
- **Task**: Classify movement quality as Good (1) or Bad (0)
#### Approaches:
- **BDense**: Flattened features for dense neural networks
- Shape: (samples, 260)
- **BCNN**: Structured features for convolutional neural networks
- Shape: (samples, 10, 13, 2) - [time_steps, joints, coordinates]
## Data Organization
The prepared data is organized in the `prepared_data/` directory:
### ADense Files (3D, Flattened):
- `A_Dense_train_X.npy`: Training features (shape: samples×390)
- `A_Dense_train_y.npy`: Training labels
- `A_Dense_test_X.npy`: Test features (shape: samples×390)
- `A_Dense_test_y.npy`: Test labels
- `A_Dense_train_aug_X.npy`: Augmented training features
- `A_Dense_train_aug_y.npy`: Augmented training labels
- `A_Dense_test_aug_X.npy`: Augmented test features
- `A_Dense_test_aug_y.npy`: Augmented test labels
### ACNN Files (3D, Structured):
- `A_CNN_train_X.npy`: Training features (shape: samples×10×13×3)
- `A_CNN_train_y.npy`: Training labels
- `A_CNN_test_X.npy`: Test features (shape: samples×10×13×3)
- `A_CNN_test_y.npy`: Test labels
- `A_CNN_train_aug_X.npy`: Augmented training features
- `A_CNN_train_aug_y.npy`: Augmented training labels
- `A_CNN_test_aug_X.npy`: Augmented test features
- `A_CNN_test_aug_y.npy`: Augmented test labels
### BDense Files (2D, Flattened):
- `B_Dense_train_X.npy`: Training features (shape: samples×260)
- `B_Dense_train_y.npy`: Training labels
- `B_Dense_test_X.npy`: Test features (shape: samples×260)
- `B_Dense_test_y.npy`: Test labels
- `B_Dense_train_aug_X.npy`: Augmented training features
- `B_Dense_train_aug_y.npy`: Augmented training labels
- `B_Dense_test_aug_X.npy`: Augmented test features
- `B_Dense_test_aug_y.npy`: Augmented test labels
### BCNN Files (2D, Structured):
- `B_CNN_train_X.npy`: Training features (shape: samples×10×13×2)
- `B_CNN_train_y.npy`: Training labels
- `B_CNN_test_X.npy`: Test features (shape: samples×10×13×2)
- `B_CNN_test_y.npy`: Test labels
- `B_CNN_train_aug_X.npy`: Augmented training features
- `B_CNN_train_aug_y.npy`: Augmented training labels
- `B_CNN_test_aug_X.npy`: Augmented test features
- `B_CNN_test_aug_y.npy`: Augmented test labels
## Data Sources
The data was extracted from the original processed sequences by interpreting the first portion of each frame as joint coordinates:
- For 3D problem: First 39 values per frame interpreted as 13 joints × 3 coordinates
- For 2D problem: First 26 values per frame interpreted as 13 joints × 2 coordinates
## Usage Examples
```python
import numpy as np
# Load ADense data for training a dense network
X_train = np.load('prepared_data/A_Dense_train_X.npy')
y_train = np.load('prepared_data/A_Dense_train_y.npy')
X_test = np.load('prepared_data/A_Dense_test_X.npy')
y_test = np.load('prepared_data/A_Dense_test_y.npy')
# Load ACNN data for training a CNN
X_train_cnn = np.load('prepared_data/A_CNN_train_X.npy')
y_train_cnn = np.load('prepared_data/A_CNN_train_y.npy')
```
## Note on Augmented Data
All datasets include both original and augmented versions:
- Original data maintains the original 91 training + 23 test samples
- Augmented data includes 4 additional versions per original sample (mirror, rotate ±10°, stretch)
- Total augmented data: 455 training + 115 test samples |