Project Description:

The IoT Elderly Fall Detection System is designed to monitor and detect accidental falls in elderly individuals using IoT sensors and AI-based motion analysis. The system immediately alerts caregivers or family members when a fall is detected, ensuring quick response and improved elderly care. It’s especially useful in smart homes, assisted living facilities, or for seniors living alone.

Core Objective:

To detect falls accurately in real-time using smart devices and send instant alerts for emergency response, minimizing risks of unattended injuries or medical emergencies.

Key Features:

1. Wearable or Stationary Sensors

   • Accelerometers, gyroscopes (e.g., MPU6050 or smartphone sensors)

   • Optional: posture monitoring via cameras or depth sensors

2. Fall Detection Algorithm

   • Uses AI/ML or threshold-based logic to detect:

     • Sudden change in motion (acceleration)

     • Inactivity after impact

     • Body orientation

3. Real-Time Alerts

   • Instant SMS, push notification, or email to caregiver/family

   • GPS location of the person (if using mobile/wearable)

4. Web Dashboard / Mobile App

   • User profile management

   • History of fall events

   • Live status: Active / Idle / Emergency

5. Emergency Protocols

   • Auto-call ambulance (optional, if integrated with service)

   • Countdown timer before alert (user can cancel if false alarm)

Tech Stack:

 AI/ML (for enhanced accuracy):

• KNN, SVM, Decision Tree for movement pattern classification

• TensorFlow/Keras if deep learning is needed

• Optional: OpenCV if camera-based fall detection is used

 Frontend:

• HTML, CSS, Bootstrap

• JavaScript for real-time dashboard

 Backend:

• PHP / Node.js / Java – for server logic

• API for alert notifications

 IoT:

• ESP32 / Raspberry Pi / Arduino + MPU6050 sensor

• Wi-Fi/Bluetooth to send data to backend

 Database:

• MySQL or MongoDB for storing fall events, user info, contacts

How It Works:

1. The system collects real-time motion data from sensors.

2. AI model (or logic) analyzes:

   • Speed of fall

   • Post-fall inactivity

   • Angle of movement

3. If fall is detected:

   • Alert is triggered after X seconds (if not canceled by user)

   • Notifications are sent to emergency contacts

4. Dashboard updates with incident timestamp and details