README.md

metadata

license: mit
task_categories:
  - feature-extraction
language:
  - en
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:

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 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
• Models: hand_classifier_*.pkl files
• Collection: collect_data.py in shared folder

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Last Updated: December 2025 Format Version: 1.0 Total Samples: 149,762