🪖 Silent Guard — Smart Helmet for Accident Detection
An IoT + AI wearable solution that detects accidents and unsafe situations in real-time, instantly alerting trusted contacts with live GPS location — built for women night shift workers.
🚨 Problem
Women working night shifts in factories, hospitals, and BPOs across India face a serious but invisible safety crisis. They deal with harassment during commute, unsafe pick-up points, and threatening situations at odd hours — yet they stay silent.
Why do they stay silent?
- Reporting to police feels scary and slow
- HR complaints risk their job
- No trusted, private system exists for them
- Existing apps require active calling — impossible when threatened
This affects 5 crore+ women across industrial towns like Coimbatore, Hosur, Chennai, and Pune.
💡 Solution
Silent Guard — an AI-powered wearable wristband + mobile app that:
- Detects accidents and falls automatically using sensors
- Allows one-tap Silent SOS without calling anyone
- Instantly sends live GPS location to trusted contacts via SMS
- Uses AI model to avoid false alerts from normal bumps
- Works without internet — GSM based
⚙️ Hardware Components
- ESP32 Main controller + WiFi
- MPU6050 Accelerometer + Gyroscope (fall detection)
- SW-420 Vibration Sensor Impact detection
- NEO-6M GPS Module Live location tracking
- SIM800L GSM Module SMS alert to trusted contacts
- Li-Po Battery Portable power supply
- Push Button Silent SOS trigger
🤖 AI/ML Model
- Framework: TensorFlow Lite (runs on ESP32)
- Input: Accelerometer + Gyroscope sensor data
- Output: Real Accident / Normal Movement (binary classification)
- Advantage: Filters out false positives like speed bumps or normal walking
🔁 How It Works
Worker wears Silent Guard wristband ↓ MPU6050 + Vibration Sensor — continuous monitoring ↓ Sudden impact OR SOS button pressed? ↓ AI Model confirms → Real Emergency or False Alarm? ↓ GPS Location fetched via NEO-6M ↓ SMS sent to 3 Trusted Contacts (NOT police first) ↓ Buzzer + LED Alert activated on device
💻 Software Stack Embedded Code: Arduino IDE (C++) AI Model Training: Python + TensorFlow Model Deployment: TensorFlow Lite on ESP32 Mobile App: MIT App Inventor (trusted contact management) SMS API: SIM800L AT Commands
🌍 Why This Hasn't Been Solved Yet
- Existing apps (Nirbhaya, 112) require active calling — impossible when threatened
- Built for general public, not specifically for night shift industrial workers
- Language barriers — most apps are English only
- Night shift workers are rarely the target user in mainstream safety tech
📊 Expected Impact
Metric Value
- Accident Detection Accuracy ~94%
- False Positive Rate <5%
- SMS Alert Time < 10 seconds
- Target Users 5 Crore+ night shift women workers in India
🏆 Submitted For
FixForward 2026 — by Hyrup Online Innovation Challenge Bannari Amman Institute of Technology, Sathyamangalam
👨💻 Team — Innovators Hub
Gokul VG - Hardware + Firmware + Team Lead Divya Nandhini V - Research + Solution Design
📞 Contact
Gokul VG ECE — 2nd Year Bannari Amman Institute of Technology 📧 gokulvg.ec24@bitsathy.ac.in
📄 License
MIT License — Free to use and modify with attribution.
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🗓️ Last Updated: March 12, 2026