SenseWave:WiFi Motion Detector

SenseWave turns your phone's Wi-Fi radio into a passive motion sensor — no camera, no microphone, complete privacy. It analyses RSSI fluctuations from nearby access points to detect movement, estimate breathing rate, track sleep quality, count people, and more. 🔬 EXPERIMENTAL SYSTEM — For education and research 📡 How It Works: • Monitors Wi-Fi signal strength (RSSI) in real-time • Detects fluctuations caused by movement through the signal path • DSP pipeline: Butterworth filtering + FFT + variance analysis • No camera required — completely passive, privacy-safe sensing ✨ 7 Detection Modes: • 📊 Radar — animated sweep, heatmap, motion history timeline • 🫁 Breathing — real-time BPM from 0.1–0.6 Hz band-pass filter • 🌙 Sleep — overnight restlessness tracking and hourly chart • 🚶 Walking — stride-frequency detection, steps/min via gait FFT • 👥 People Counter — estimates 0–4+ occupants from variance • 📈 Analytics — FFT spectrum, SNR, scan rate, CSV export • 🔧 Calibration — guided baseline calibration saved on-device ⚠️ Important Limitations: • Cannot identify WHO moved (person, pet, object) • Cannot determine WHERE movement occurred • Cannot measure distance to motion • Subject to false positives from interference • Accuracy varies by environment and wall materials • Android limits Wi-Fi scan frequency 🔒 Privacy First: • No camera or microphone access • No data collection or transmission • All processing 100% on-device • No tracking, no analytics • Works completely offline 📱 Requirements: • Android 8.0 or higher • Physical Android device (emulator not supported) • Wi-Fi enabled with at least one nearby access point • Location permission (required by Android for Wi-Fi scanning) 🎯 Best For: • Educational demonstrations of Wi-Fi physics • Experimental motion and presence sensing projects • Privacy-preserving occupancy detection • Research into RF signal processing SenseWave demonstrates Wi-Fi sensing principles using standard Android APIs. It is a learning tool and experiment — not a security system. Results vary based on environment, wall materials, and signal conditions. Perfect for students, researchers, hobbyists, and anyone curious about Wi-Fi technology and signal processing!