IMU Analysis: Walking vs Backward Fall Detection using Allan Deviation | Python

🚶‍♂️💥 IMU Sensor Analysis: Walking vs Backward Fall Detection using Allan Deviation In this tutorial, we analyze gyroscope data from the UMAFall dataset to distinguish between normal walking and a backward fall using Allan Deviation - a powerful statistical tool originally developed for atomic clocks! 📊 WHAT YOU'LL LEARN: ✓ How to load and preprocess IMU sensor data (UMAFall dataset) ✓ Convert angular velocity to orientation angles using integration ✓ Calculate Signal Energy for movement intensity comparison ✓ Implement Allan Deviation from scratch in Python ✓ Interpret log-log plots to identify noise characteristics ✓ Use these metrics for fall detection algorithms 🔧 CODE FEATURES: - Full Python implementation using NumPy & Matplotlib - Allan Deviation function with geometric tau spacing - Comparison of walking vs falling motion patterns - Visualization of raw signals and Allan Deviation plots 📐 KEY FORMULAS COVERED: 1. Angle Integration: θ = Σ(ω × Δt) 2. Signal Energy: E = (1/N) × Σ(ωx² + ωy² + ωz²) 3. Allan Deviation (Full Formula): ADEV(τ) = √[ 1/(2τ²(N-2m)) × Σ(θᵢ₊₂ₘ - 2θᵢ₊ₘ + θᵢ)² ] 📁 DATASET: UMAFall Dataset - Subject 02 - Walking: Normal ambulation - Fall: Backward fall from standing position - Sampling Rate: 100 Hz 🖥️ CODE AVAILABLE: GitHub Repository: [INSERT LINK] 📚 TIMESTAMPS: 0:00 - Introduction 0:45 - Understanding the UMAFall Dataset 2:00 - Loading & Preprocessing Data 3:30 - Converting Gyro to Angles (Integration) 5:00 - Signal Energy Calculation 6:30 - Allan Deviation - The Core Equation 10:00 - Python Implementation Walkthrough 12:30 - Interpreting Results & Plots 15:00 - Comparing Walking vs Fall Patterns 17:00 - Practical Applications & Conclusions 18:30 - Outro 🔗 RELATED VIDEOS: - Kalman Filtering for IMU Sensors: [LINK] - Sensor Fusion Explained: [LINK] - Fall Detection using Machine Learning: [LINK] 📖 REFERENCES: - IEEE Standard for Allan Deviation (IEEE Std 952-1997) - UMAFall Dataset Paper: [DOI LINK] - "Understanding Allan Deviation" - NIST Publication 💻 REQUIREMENTS: - Python 3.7+ - NumPy - Matplotlib - UMAFall Dataset (free download) 👨‍💻 FOLLOW ME: GitHub: [LINK] Twitter: [LINK] Personal Website: [LINK] 📧 Business Inquiries: [EMAIL] #IMU #AllanDeviation #FallDetection #SensorFusion #Python #Gyroscope #DataScience #WearableTech #SignalProcessing #UMAFall ⚠️ DISCLAIMER: This content is for educational purposes. Always consult medical professionals for fall risk assessment and detection systems. 🔥 LIKE & SUBSCRIBE for more sensor analysis tutorials!