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Create a fall detection system using OpenCV and deep learning to identify accidental falls, especially for elderly care. Send instant alerts via email/SMS.
Intermediate-level. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to land your first computer vision job or freelance clients.
Learn how AI and computer vision can identify and respond to human falls.
Use Python with OpenCV and pre-trained deep learning models to detect body posture.
Develop a real-time system to monitor live video and detect falls instantly.
Store fall events with date and time in a database for alert and monitoring purposes.
Build a user-friendly GUI to display live video and send fall alerts visually.
Finish the course and receive a verified certificate of success.
Muhammad Yaqoob is the founder of Tentosoft Pvt Ltd and a seasoned Computer Vision expert. With 10+ years of experience and over 5,000+ students taught globally, he brings deep industry knowledge and a passion for practical, hands-on learning.
8 weeks of comprehensive training with 50+ lessons and 10+ hours of content
This section introduces the concept of fall detection using AI, explaining how the system works to recognize and alert when a person falls, particularly useful in healthcare and elderly monitoring applications.
Course Overview and Features
AI + Computer Vision Approach
Data-Driven Accuracy
Setting up Python and its development tools is the foundation of building AI applications. This ensures the environment is ready to run and develop object detection models.
Installing Python
VS Code Setup for Python Development
This module outlines the structure and goals of the project, providing a roadmap for how each component fits into the complete fall detection and alert workflow.
Fall Detection
Modular Architecture
Scalable and Extendable
Learn about the Python libraries and packages used to power the fall detection system, including computer vision, deep learning, and backend APIs.
Understanding Key Packages for Object Detection
OpenCV
Custom Utility Modules
This section covers installing MQTT and configuring it for real-time message communication, allowing alerts to be sent instantly when a fall is detected.
System Installation and MQTT Configuration
MQTT Broker Setup
Test the MQTT Workflow
Build a secure API that allows users to register and log in, enabling personalized access and tracking for multiple users within the system.
Implementing User Registration & Login API
Database Integration
Security Best Practices
Combine Flask and MQTT to create a seamless backend where data can be processed, alerts triggered, and real-time communication established efficiently.
Implementing MQTT & Flask Integration
Architecture Overview
Benefits of Integration
Understand the core logic that powers fall detection using machine learning or computer vision models. This section focuses on how the system identifies a fall event.
Concept Behind Fall Detection
Object Detection + Bounding Box
Triggering Alerts
This part shows how the detection model is exposed through an API, handling real-time inputs and returning predictions to trigger necessary actions.
Implementing the Prediction API
MQTT / SocketIO
API Flow
Backend API Logic
Centralizes your ML model behind a secure interface
Test and validate your system to ensure every component—detection, APIs, alerts—is functioning as expected through various simulated fall scenarios.
Running the Code & System Testing
Test with Non-Fall Movements
Test API Routes
Handle Error Conditions
Is This Course Right for You?
Kickstart your AI journey with structured, hands-on learning.
Build a portfolio that recuriters can't ignore.
Add powerful AI/CV features to your apps and software.
Upskill for higher-paying, future-ready tech roles.
Build Smarter, more intelligent applications.
Transition into AI even with zero background.
One-time payment for lifetime access to all course materials and updates
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Highly recommended for small teams who seek to upgrade their time & perform.
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