🚶‍♂️ Acceleration-based User Authentication System

Behavioral Biometric Authentication using Gait Analysis

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📖 Project Overview

This project implements a behavioral biometric authentication system that identifies and verifies users based on their unique walking patterns (gait). Unlike traditional authentication methods (passwords, fingerprints), this system uses continuous sensor data from smartphone accelerometers and gyroscopes to authenticate users passively during normal walking.

🎯 Achievement: Successfully reduced Equal Error Rate (EER) to 3.43% with 92.68% accuracy using multi-sensor fusion and optimized feature selection.

🎯 Key Objectives

• 🔐 User Authentication: Classify 10 different users with high accuracy
• 📅 Cross-Day Robustness: Ensure models trained on one day work effectively on different days
• 🌍 Real-World Feasibility: Handle varying conditions, walking speeds, and temporal variations
• ⚡ Optimization: Achieve minimal Equal Error Rate (EER) through systematic optimization

🌟 Key Features

✅ Multi-Sensor Fusion: Combines accelerometer and gyroscope data
✅ Rich Feature Extraction: 112 time and frequency domain features
✅ Deep Learning Classification: Multi-Layer Perceptron (MLP) with optimized architecture
✅ Gait-Specific Analysis: Filters walking segments from non-walking activities
✅ Cross-Day Validation: Rigorous testing across different days
✅ Systematic Optimization: Feature selection, architecture tuning, and data augmentation

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📊 Dataset Information

📥 Data Collection

• 👥 Users: 10 participants
• 📆 Days: 2 collection sessions per user
• 📱 Sensors: 6-axis IMU (3-axis accelerometer + 3-axis gyroscope)
• 📲 Device: Smartphone (waist-mounted)
• ⚙️ Sampling Rate: Variable (resampled to 30 Hz)
• 🚶 Activity: Natural walking patterns

📈 Data Statistics

• 📄 Total Files: 20 CSV files (10 users × 2 days)
• 📊 Segments per User/Day: ~71 segments (5-second windows)
• 🎯 Total Training Samples: ~1,420 segments
• 🔢 Features per Segment: 112 (combined variant)

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📂 Project Structure

project_root/
├── 📁 data_raw/                  # Raw CSV sensor data (10 Users × 2 Days)
│   ├── U1NW_FD.csv              # User 1, Full Day
│   ├── U1NW_MD.csv              # User 1, Morning Data
│   └── ...
├── 📁 data_segmented/            # Segmented .mat files (5s windows, 0% overlap)
│   └── User{X}_Day{Y}_segments.mat
├── 📁 data_segmented_50overlap/  # Segmented .mat files (5s windows, 50% overlap)
├── 📁 features/                  # Extracted features organized by sensor type
│   ├── accel/                   # Accelerometer features only (45 features)
│   ├── gyro/                    # Gyroscope features only (45 features)
│   └── combined/                # Multi-modal features (112 features)
├── 📁 templates/                 # Pre-processed, balanced datasets for training
│   ├── accel/AllTemplates_accel.mat
│   ├── gyro/AllTemplates_gyro.mat
│   └── combined/
│       ├── AllTemplates_combined.mat
│       └── AllTemplates_combined_gait.mat  # Gait-filtered data
├── 📁 models/                    # Trained Neural Network models
│   └── combined/baseline_mlp.mat
├── 📁 results/                   # Evaluation metrics and analysis
│   ├── combined/                # Scenario-wise metrics (CSV)
│   ├── optimization/            # Optimization experiment results
│   ├── plots/                   # Generated visualizations (PNG)
│   └── variants_comparison.csv  # Cross-variant performance
├── 📁 scripts/                   # MATLAB Source Code
│   ├── Script1_FeatureExtraction.m       # Stage 1-4: Preprocessing & Features
│   ├── Script2_TemplateGeneration.m      # Stage 5: Template Generation
│   ├── Script3_ClassificationEvaluation.m # Stage 6-9: Training & Evaluation
│   └── Plots.m                           # Visualization generation
└── 📄 PROJECT_STRUCTURE.md       # Detailed structure documentation

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🚀 Getting Started

Prerequisites

• MATLAB (R2021a or later recommended)
• Deep Learning Toolbox (for patternnet, train)
• Signal Processing Toolbox (for pwelch, bandpower)
• Statistics and Machine Learning Toolbox (for cvpartition, fitcecoc)

⚙️ Execution Workflow

Run the scripts in the following order to reproduce the full pipeline:

1️⃣ Feature Extraction

Script: scripts/Script1_FeatureExtraction.m

• Input: data_raw/*.csv
• Process:
  • Loads raw sensor data.
  • Resamples signals to a fixed 30 Hz.
  • Segments data into 5-second windows (150 samples).
  • Extracts Time-Domain Features (Mean, Std, RMS, Skewness, Kurtosis, etc.).
  • Extracts Frequency-Domain Features (Bandpower, Energy, Spectral Entropy, etc.).
• Output: data_segmented/, features/

2️⃣ Template Generation

Script: scripts/Script2_TemplateGeneration.m

• Input: features/
• Process:
  • Aggregates features from all users.
  • Performs Gait Filtering (removes non-walking segments based on spectral energy).
  • Checks for class imbalance and applies downsampling if necessary.
  • Generates unified datasets for Accel, Gyro, and Combined variants.
• Output: templates/

3️⃣ Classification & Evaluation

Script: scripts/Script3_ClassificationEvaluation.m

• Input: templates/
• Process:
  • Training: Trains a Multi-Class MLP (Feed-Forward Network).
  • Evaluation: Calculates FAR, FRR, EER, and Accuracy for 3 scenarios.
  • Optimization: Experiments with feature selection (Fisher Score), network architecture, and split ratios.
  • Comparison: Compares Accel-only vs. Gyro-only vs. Combined performance.
• Output: models/, results/

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🧠 Methodology

1. Preprocessing

• Resampling: Linear interpolation to 30 Hz to handle irregular sensor sampling.
• Segmentation: Fixed-width sliding windows (5 seconds).
• Overlap: Supports 0% (baseline) and 50% (optimized) overlap.

2. Feature Engineering

The system extracts 70+ features per segment to capture unique gait characteristics:

Domain	Features Extracted
Time Domain	Mean, Std Dev, Min, Max, RMS, Variance, Range, 75th Percentile, Skewness, Kurtosis, IQR, Vector Magnitude stats.
Frequency Domain	Bandpower (0.5-3Hz), Total Energy, Dominant Frequency, Spectral Centroid, Spectral Entropy, High-band Power (3-10Hz), Dominant Amplitude.

3. Machine Learning Model

• Classifier: Pattern Recognition Network (patternnet)
• Architecture: Multi-Layer Perceptron (MLP)
  • Input Layer: ~112 features (Combined variant)
  • Hidden Layers: [128, 64] neurons (Baseline)
  • Output Layer: 10 neurons (Softmax for 10 users)
• Training Algo: Scaled Conjugate Gradient (trainscg)

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🔬 Experimental Results & Analysis

1. Variant Comparison (Cross-Day Scenario)

The system was tested on the challenging Cross-Day scenario (Train on Day 1, Test on Day 2) to evaluate real-world robustness.

Variant	EER (%)	Accuracy (%)	FAR	FRR
Combined (Accel+Gyro)	4.45%	88.45%	0.0136	0.1204
Accelerometer Only	8.85%	79.72%	0.0227	0.2030
Gyroscope Only	19.49%	60.85%	0.0442	0.3961

Observation: The Combined variant significantly outperforms single-sensor approaches, reducing the Equal Error Rate (EER) by nearly half compared to Accelerometer-only.

2. Optimization Experiments

Several optimization levers were tested to improve performance beyond the baseline.

Feature Selection (Rank-Average Fisher Score)

Reducing the feature set to the most discriminative features improved generalization.

Features Kept	EER (%)	Accuracy (%)
Top-30	4.47%	84.93%
Top-52	3.90%	90.42%
Top-65 (Best)	3.43%	92.68%
All (112)	4.45%	88.45%

Network Architecture

Simpler architectures proved robust, but the baseline [128, 64] remained competitive.

• [128] (Single Layer): EER 4.20%, Acc 89.72%
• [128, 64] (Baseline): EER 3.90%, Acc 88.03%

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🏆 Final Evaluation

The final optimized system utilizes the Combined sensor data with Top-65 ranked features and a [128, 64] MLP architecture.

Final Performance Metrics (Cross-Day)

• Equal Error Rate (EER): 3.43%
• Classification Accuracy: 92.68%
• False Acceptance Rate (FAR): 0.0093
• False Rejection Rate (FRR): 0.0817

This performance demonstrates that the system is highly effective at authenticating users even on different days, validating the stability of the extracted gait features.

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📊 Evaluation Scenarios

The system is evaluated under three distinct scenarios to test robustness:

Scenario	Description	Purpose
Scenario 1: Same-Day	Train on 70% of Day 1, Test on 30% of Day 1.	Baseline performance check.
Scenario 2: Cross-Day	Train on Day 1, Test on Day 2.	Real-world simulation. Tests temporal stability of gait.
Scenario 3: Combined	Train/Test on mixed data from both days.	Tests overall data variance handling.

Key Metrics

• FAR (False Acceptance Rate): Likelihood of an impostor being accepted.
• FRR (False Rejection Rate): Likelihood of a genuine user being rejected.
• EER (Equal Error Rate): The point where FAR = FRR (Lower is better).
• Accuracy: Overall correct classification percentage.

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📈 Results Summary

• Best Variant: Combined (Accelerometer + Gyroscope) yielded the lowest baseline EER of 4.45%.
• Optimization Success: Feature selection (Top-65) further reduced EER to 3.43%.
• Robustness: The system maintained >92% accuracy even when tested on data from a different day (Cross-Day scenario).

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📊 Visualizations Gallery

The project generates 8 comprehensive visualizations to document system performance. All plots are saved in results/plots/ directory.

Plot	Description	Preview
1. EER Curve (Cross-Day)	FAR and FRR curves vs. threshold with EER point marked. Shows the trade-off between false acceptance and false rejection rates.
2. Overlap Comparison	Cross-Day EER comparison between 0% and 50% window overlap. Demonstrates impact of data augmentation.
3. Variant Comparison	Performance comparison across Accel-only, Gyro-only, and Combined variants.
4. Feature Selection	EER vs. number of features used. Shows optimal feature count (Top-65) for best performance.
5. Architecture Analysis	Comparison of different MLP architectures ([64], [128], [128,64], etc.) on Cross-Day scenario.
6. Scenario Comparison	EER across Same-Day, Cross-Day, and Combined evaluation scenarios.
7. Sample Acceleration Window	Raw accelerometer signal (X, Y, Z axes) from a 5-second walking segment.
8. Gait Filtering Impact	EER comparison between all data vs. gait-filtered data in Cross-Day scenario.

Key Findings from Visualizations

📊 Plot 1 (EER Curve): Optimal threshold is ~0.45, achieving EER of 4.45%
📊 Plot 2 (Overlap): 50% overlap reduces EER compared to 0% overlap
📊 Plot 3 (Variants): Combined sensors reduce EER by 50% vs. accelerometer-only
📊 Plot 4 (Features): Top-65 features achieve lowest EER (3.43%), better than using all 112
📊 Plot 5 (Architecture): Single hidden layer [128] performs comparably to [128,64]
📊 Plot 6 (Scenarios): Cross-Day is hardest (EER 4.45%), Same-Day easiest (EER 3.05%)
📊 Plot 7 (Raw Signal): Shows typical gait periodicity in acceleration patterns
📊 Plot 8 (Gait Filtering): Gait-only segments improve authentication accuracy

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🚀 How to Run

Quick Start

1. Clone/Download the project
2. Open MATLAB and navigate to project root
3. Run scripts in order:

% Step 1: Feature Extraction (takes ~5-10 minutes)
cd scripts
run('Script1_FeatureExtraction.m')

% Step 2: Template Generation (takes ~1 minute)
run('Script2_TemplateGeneration.m')

% Step 3: Classification & Evaluation (takes ~10-15 minutes)
run('Script3_ClassificationEvaluation.m')

% Step 4: Generate Visualizations (takes ~1 minute)
run('Plots.m')

Expected Output

✅ data_segmented/ - 20 .mat files (segmented data)
✅ features/ - 60 .mat files (time + freq features for 3 variants)
✅ templates/ - 4 .mat files (training-ready datasets)
✅ models/ - 1 .mat file (trained baseline MLP)
✅ results/ - 10+ CSV files + 8 PNG plots

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📚 Key Insights & Lessons Learned

✅ What Worked Well

1. Multi-Modal Fusion: Combining accelerometer and gyroscope reduced EER by 50%
2. Feature Selection: Less is more - 65 features outperformed 112 features
3. Gait Filtering: Removing non-walking segments improved cross-day robustness
4. Simple Architecture: [128, 64] MLP proved sufficient (no need for deep networks)
5. Proper Evaluation: Cross-day testing revealed true real-world performance

⚠️ Challenges Encountered

1. Irregular Sampling: Raw sensor data required careful resampling
2. Class Imbalance: Minor variations required downsampling
3. Temporal Drift: Cross-day performance naturally lower than same-day
4. Feature Redundancy: Many extracted features were highly correlated
5. Overfitting Risk: Larger networks performed worse due to limited training data

🔮 Future Improvements

• More Users: Expand dataset to 20-50 users for better generalization
• Longer Duration: Collect data over 1-2 weeks to test long-term stability
• Real-Time Processing: Implement sliding window inference
• Mobile Deployment: Port model to TensorFlow Lite for smartphone apps
• Additional Activities: Test robustness with running, stairs, sitting
• Transfer Learning: Pre-train on large public gait datasets

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🤝 Contributing

We welcome contributions from the community! Whether you're fixing bugs, adding features, improving documentation, or suggesting enhancements, your help is appreciated.

How to Contribute

• 🐛 Report Bugs: Open an issue with detailed information
• ✨ Suggest Features: Share your ideas for improvements
• 💻 Submit Code: Fork, create a branch, and submit a pull request
• 📖 Improve Docs: Help make documentation clearer and more comprehensive

Please read our CONTRIBUTING.md for detailed guidelines on:

• Code style and standards
• Development workflow
• Testing requirements
• Pull request process
• Community guidelines

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📄 License & Usage

This is a research project. Feel free to use the code and methodology for educational purposes.

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🛠️ Author

Project Developer
Date: November 2025

📞 Contact & Acknowledgments

For questions about the methodology or implementation details, please refer to the comprehensive code comments in the MATLAB scripts.

Note: This project demonstrates the feasibility of behavioral biometric authentication using smartphone sensors and achieves state-of-the-art performance (EER < 5%) suitable for practical deployment.

For detailed performance graphs and visual analysis, check the results/plots/ directory.

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🌟 Star History

If you found this project helpful, please consider giving it a star! ⭐

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Made with ❤️ and MATLAB

Transforming Walking Patterns into Secure Authentication 🚶‍♂️🔐

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_{© 2025 • Acceleration-based User Authentication System • Behavioral Biometrics Research}