🛡️ Fall Detection System

The Fall Detection System is an IoT and machine learning-based solution designed to monitor elderly individuals and detect potential falls in real-time using multiple environmental and motion sensors. The system aims to alert caregivers and reduce the risks associated with undetected falls.

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📦 Features

• Real-time fall detection using motion (MPU6050) and distance (HC-SR04) sensors.
• Environmental data collection using BMP280 (temperature & pressure sensor).
• LED alert system for immediate visual feedback.
• Data logging support for training machine learning models.
• Expandable and modular system for future integrations.

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⚙️ Hardware Components

• Arduino Uno R3
• MPU6050 (Accelerometer + Gyroscope)
• HC-SR04 Ultrasonic Sensor
• BMP280 (Temperature + Pressure sensor)
• LED
• Breadboard & jumper wires

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🧠 Machine Learning Integration

The collected sensor data can be used to:

• Train ML models like Random Forest, XGBoost, or a CNN-LSTM hybrid model.
• Detect fall events using engineered features like jerk, acceleration magnitude, and environment context.

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🛠️ Installation & Setup

1. Hardware Setup

Refer to the above schematic image and connect:

• MPU6050: Uses I2C (A4 -> SDA, A5 -> SCL)
• BMP280: Also uses I2C (shared lines)
• HC-SR04: Trig -> D9, Echo -> D10
• LED: Anode -> D8 via a 220Ω resistor

2. Software Setup

1. Install the Arduino IDE.
2. Install required libraries:
   • Adafruit_BMP280
   • Adafruit_Sensor
   • Wire.h
   • MPU6050
3. Upload the fall_detection.ino sketch to your Arduino Uno.

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📈 Data Collection

Sensor data logged includes:

• ax, ay, az – Accelerometer axes
• gx, gy, gz – Gyroscope axes
• distance – Ultrasonic sensor
• pressure, altitude – BMP280
• acc_magnitude, jerk, label – Engineered features

Use the data to train ML models in Python using scikit-learn, xgboost, or tensorflow.