Ai-Based Human Fall Detection System Using Python And Opencv

SafeFall: AI-Powered Fall Detection & Alert System with Python & Computer Vision.

What you'll learn

Understand the fundamentals of fall detection using computer vision and its significance in enhancing elderly care, workplace safety, and real-time monitoring.

Set up a Python development environment with essential libraries, including OpenCV and MediaPipe, for real-time human pose estimation and fall detection.

Explore the YOLOv8n model for accurate and efficient person detection in live video streams.

Utilize MediaPipe to extract human skeletal key points for precise fall detection.

Learn preprocessing techniques for video frames, including normalization and resizing, to improve model performance and real-time processing efficiency.

Implement real-time visualization of detection outputs by annotating video frames with bounding boxes, skeletal structures, and fall alerts.

Address challenges such as occlusions, varying camera angles, and differences in body postures to improve detection accuracy.

Develop an MQTT-based real-time alert system that notifies caregivers or emergency responders when a fall is detected.

Integrate a SQL database for storing user details, system logs, and incident reports for data analysis and tracking.

Deploy the system using Flask for backend operations, ensuring smooth data flow and API-based communication with mobile or web applications.

Requirements

Basic understanding of Python programming (helpful but not mandatory).

A laptop or desktop computer with internet access [Windows OS with Minimum 4GB of RAM).

No prior knowledge of AI or Machine Learning is required—this course is beginner-friendly.

Enthusiasm to learn and build practical projects using AI and IoT tools.

Description

Welcome to the AI-Powered Fall Detection & Alert System with YOLOv8, MediaPipe, and Flask course! • In this hands-on course, you'll learn how to build a real-time fall detection system using YOLOv8 for person detection, MediaPipe for skeleton analysis, and Flask for backend processing. This system is designed for elderly care, workplace safety, and real-time emergency monitoring, providing accurate fall detection and instant alerts.• This course focuses on leveraging YOLOv8 for detecting individuals and MediaPipe for analyzing skeletal movement, ensuring accurate fall detection based on shoulder and leg angle calculations. By the end of the course, you’ll have developed a fully functional real-time fall detection and alert system that integrates Flask, MQTT-based notifications, and SQL for user management.What You'll Learn:• Set up your Python development environment and install essential libraries like OpenCV, MediaPipe, Flask, and MQTT for seamless integration.• Use the YOLOv8 model to detect human presence and track movements in live video feeds.• Leverage MediaPipe for extracting skeletal points and calculating shoulder and leg angles to determine falls.• Preprocess video streams to enhance detection performance, handling variations in lighting, camera angles, and occlusions.• Implement a real-time visualization system, displaying detected falls with bounding boxes and alerts.• Develop an MQTT-based notification system to instantly alert caregivers, security personnel, or emergency responders when a fall occurs.• Integrate a SQL database to store user details, incident logs, and system alerts for better monitoring and analysis.• Deploy the system using Flask, ensuring smooth real-time data processing and API communication with a mobile or web-based dashboard.• Optimize the system for real-time performance, handling multiple video streams efficiently.Enroll today and start building your SafeFall: AI-Powered Fall Detection & Alert System

Overview

Section 1: Introduction to AI-Powered Fall Down Detection & Alert System

Lecture 1 Course Introduction and Features

Section 2: Environment Setup for Python Development

Lecture 2 Installing Python

Lecture 3 VS Code Setup for Python Development

Section 3: Fall Down Detection System Project Overview

Lecture 4 Fall Down Detection

Section 4: Dependency & Package Overview

Lecture 5 Required Dependencies and Installation

Section 5: Installation & MQTT Setup

Lecture 6 System Installation and MQTT Configuration

Section 6: User Registration & Login API

Lecture 7 Implementing User Registration & Login API

Section 7: MQTT & Flask Integration

Lecture 8 Implementing MQTT & Flask Integration

Section 8: Fall Detection Logic

Lecture 9 Implementing Fall Detection Logic

Section 9: Prediction API Workflow

Lecture 10 Implementing the Prediction API

Section 10: Code Execution & Testing

Lecture 11 Running the Code & System Testing

Section 11: Wrapping Up

Lecture 12 Course Wrap-Up

Students looking to explore AI and its practical applications in fall detection using YOLOv8 and MediaPipe for real-time monitoring and emergency response.,Working professionals wanting to upskill in AI, Machine Learning, and Python programming for developing safety and healthcare-related applications.,IoT enthusiasts who want to integrate AI-powered fall detection into smart home systems, wearable devices, or emergency alert solutions.,Aspiring developers aiming to build a career in computer vision, real-time monitoring systems, and AI-driven healthcare solutions.