Update README.md

README.md

@@ -7,4 +7,167 @@ language:
 tags:
 - sensor
 - physics
----
+---
+
+
+# Hand Detection Training Data
+
+This folder contains sensor data collected from mobile devices for training the hand detection model.
+
+## Overview
+
+The dataset includes accelerometer and gyroscope readings from 2 subjects, each holding a device with both their left and right hands. This data is used to train the Random Forest classifier that achieves 94.6% accuracy in detecting which hand is holding the device.
+
+## Directory Structure
+
+```
+hand_data/
+├── accelerometer/          # Accelerometer sensor data (primary)
+│   ├── s-1_left_hand.csv   # Subject 1, left hand (39,102 samples)
+│   ├── s-1_right_hand.csv  # Subject 1, right hand (30,528 samples)
+│   ├── s-2_left_hand.csv   # Subject 2, left hand (44,724 samples)
+│   └── s-2_right_hand.csv  # Subject 2, right hand (35,408 samples)
+│
+└── gyrocop/                # Gyroscope data (supplementary)
+    ├── s-1_left_hand.csv   # Subject 1, left hand
+    └── s-1_right_hand.csv  # Subject 1, right hand
+```
+
+## Data Format
+
+### Accelerometer Data
+
+Each CSV file contains timestamped 3-axis accelerometer readings:
+
+| Column    | Type      | Description                              |
+|-----------|-----------|------------------------------------------|
+| timestamp | datetime  | ISO 8601 format (e.g., 2025-12-27T09:13:07.598506) |
+| x         | float     | X-axis acceleration (m/s²)               |
+| y         | float     | Y-axis acceleration (m/s²)               |
+| z         | float     | Z-axis acceleration (m/s²)               |
+
+**Example:**
+```csv
+timestamp,x,y,z
+2025-12-27T09:13:07.598506,0.849452,3.895515,8.087741
+2025-12-27T09:13:08.083118,0.727418,4.000800,8.099705
+```
+
+### Gyroscope Data
+
+Similar structure with angular velocity measurements (°/s).
+
+## Dataset Statistics
+
+### Total Samples
+- **Subject 1 (Left)**: 39,102 samples
+- **Subject 1 (Right)**: 30,528 samples
+- **Subject 2 (Left)**: 44,724 samples
+- **Subject 2 (Right)**: 35,408 samples
+- **Total**: 149,762 samples
+
+### Collection Method
+- Device: Mobile phone with accelerometer sensor
+- Sampling rate: ~50-100 Hz (varies)
+- Duration: Multiple sessions per subject/hand
+- Environment: Normal daily usage patterns
+
+## Data Characteristics
+
+### X-Axis (Left/Right Tilt)
+- **Primary discriminator** for hand detection
+- Left hand: Positive values (device tilts right)
+- Right hand: Negative values (device tilts left)
+- Statistical significance: p < 0.000001
+
+### Y-Axis (Forward/Backward Tilt)
+- Secondary feature
+- Shows hand-specific patterns
+- Less discriminative than X-axis
+
+### Z-Axis (Vertical)
+- Represents gravity component
+- Generally around 9.8 m/s² when stationary
+- Varies with device orientation
+
+### Magnitude
+- Calculated: √(x² + y² + z²)
+- Overall movement intensity
+- Helps distinguish activity levels
+
+## Usage in Training
+
+This data is used in [../which_hand_you_use.ipynb](https://github.com/rockerritesh/sensor/blob/main/hand/which_hand_you_use.ipynb) for:
+
+1. **Exploratory Data Analysis (EDA)**
+   - Distribution analysis
+   - Statistical testing
+   - Correlation analysis
+   - Time series visualization
+
+2. **Feature Engineering**
+   - Calculate magnitude
+   - Window-based statistics (mean, std, min, max)
+   - Temporal features (deltas, trends)
+
+3. **Model Training**
+   - Single-point Random Forest (94.6% accuracy)
+   - Windowed Random Forest (96%+ accuracy)
+   - PCA for visualization
+
+## File Sizes
+
+- `s-1_left_hand.csv`: ~2.1 MB
+- `s-1_right_hand.csv`: ~1.7 MB
+- `s-2_left_hand.csv`: ~2.4 MB
+- `s-2_right_hand.csv`: ~2.0 MB
+
+**Total**: ~8.2 MB (accelerometer only)
+
+## Data Quality
+
+### Completeness
+✅ No missing values
+✅ Continuous timestamps
+✅ Consistent format across all files
+
+### Statistical Validation
+✅ Normal distribution per axis
+✅ Significant hand differences (p < 0.05)
+✅ Consistent patterns across subjects
+
+## Privacy & Ethics
+
+- Data collected with informed consent
+- No personally identifiable information
+- Used solely for research purposes
+- Anonymized subject identifiers (S1, S2)
+
+## Collection Guidelines
+
+If collecting additional data:
+
+1. **Consistency**: Use same device/settings
+2. **Duration**: Minimum 5-10 minutes per hand
+3. **Activity**: Natural usage (browsing, typing, etc.)
+4. **Labeling**: Clear hand identification
+5. **Format**: Match existing CSV structure
+
+## Notes
+
+- This data is **excluded from git** (see `.gitignore`)
+- Keep data locally or use Git LFS for large files
+- Model files are generated from this data
+- Data collection scripts in `shared/` folder
+
+## Related Files
+
+- **Training**: [../which_hand_you_use.ipynb](https://github.com/rockerritesh/sensor/blob/main/hand/which_hand_you_use.ipynb)
+- **Models**: `hand_classifier_*.pkl` files
+- **Collection**: `collect_data.py` in shared folder
+
+---
+
+**Last Updated**: December 2025
+**Format Version**: 1.0
+**Total Samples**: 149,762