🚨 Fall Detection System

AI-powered Fall Detection using Arduino, MPU6050 and TinyML

# 🚨 Fall Detection System

An AI-powered Fall Detection System built using Arduino and motion sensors to detect accidental falls in real time. The project uses a trained machine learning model to classify sensor data and identify fall events.

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📌 Features

• 📈 Real-time fall detection
• 🤖 Machine learning-based classification
• ⚡ Runs on Arduino-compatible hardware
• 📊 Motion sensor data processing
• 🔍 Lightweight and efficient inference

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📂 Project Structure

Fall-Detection-System//
│
├── Fall-Detection-System//
│   ├── config.h
│   ├── fall_classifier.h
│   ├── fall_detection.ino
│   └── model.h
│
├── python_tools/
│
└── README.md

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🛠 Hardware Requirements

• Arduino-compatible board
• MPU6050 Accelerometer & Gyroscope
• USB Cable
• Computer with Arduino IDE

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💻 Software Requirements

• Arduino IDE
• Required Arduino libraries
• Python 3.x (for data preprocessing and model tools)

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/badivana/Fall-Detection-System.git

2. Open the Arduino project

Open:

Fall-Detection-System/fall_detection/fall_detection.ino

using the Arduino IDE.

3. Install required libraries

Install the libraries required by the project using the Arduino Library Manager.

4. Upload the code

• Select your board.
• Select the correct COM port.
• Upload the sketch.

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⚙ How It Works

1. Motion data is collected from the MPU6050 sensor.
2. The sensor readings are processed.
3. The embedded machine learning model classifies the motion.
4. If a fall is detected, the system triggers an alert or detection event.

Work Flow

MPU6050
    │
    ▼
Acceleration + Gyroscope
    │
    ▼
Feature Extraction
    │
    ▼
Embedded TinyML Model
    │
    ▼
Fall Classification
    │
    ▼
Alert Generation

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Hardware Workflow

MPU6050
    │
    ▼
Arduino
    │
    ▼
TinyML Model
    │
    ▼
Decision

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📁 Files

File	Description
fall_detection.ino	Main Arduino program
config.h	Configuration settings
model.h	Embedded ML model
fall_classifier.h	Classification logic
python_tools/	Python utilities for model preparation

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🎯 Applications

• Elderly care
• Patient monitoring
• Smart healthcare
• Home safety systems
• Wearable devices

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⚠️ Limitations

• Requires proper sensor calibration
• Performance depends on model quality
• Tested with MPU6050 sensor
• Does not include emergency communication

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📈 Future Improvements

• Mobile app notifications
• IoT cloud integration
• GPS-based emergency alerts
• Higher accuracy ML model
• Battery optimization

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👨‍💻 Author

Prajwal B T

Information Science Engineering Student

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⭐ Support

If you found this project useful, consider giving it a ⭐ on GitHub.