Human Fall Detection System using YOLO and Pose Estimation

📧 Project Description

This project implements an intelligent Fall Detection System that leverages YOLO-based pose estimation to monitor human movements in real time.
By analyzing keypoint coordinates (such as ankles, knees, and torso), the system determines whether a person has fallen based on the relative vertical positions of the keypoints.

The solution uses Ultralytics YOLO for detecting human poses and Supervision (sv) for visual annotation of keypoints and skeletal edges.
If the posture suggests a fall (based on average differences between keypoint positions and ankle height), the system displays a red alert box with the label “Fall Detected” on the video feed.

The system supports configurable parameters via a config.yaml file — including video source, YOLO model path, and sensitivity threshold — allowing easy tuning for different environments or camera setups.

🎯 Core Objectives

• 🧍 Detect human pose keypoints using YOLO pose estimation models.

• 📉 Analyze positional relationships between body keypoints to infer falls.

• ⚙️ Allow configurable sensitivity thresholds for detection accuracy.

• 📹 Support both recorded videos and live camera streams.

• 🖥️ Display annotated visualization with edges, keypoints, and bounding boxes.

• 🚨 Highlight detected falls in real time with visual alerts.

✨ Key Features

✅ Real-Time Pose Estimation: Uses YOLO for efficient detection of human keypoints (ankles, knees, torso, etc.).
✅ Keypoint Analysis Algorithm: Calculates positional differences to identify unnatural or fallen poses.
✅ Custom Sensitivity Control: Adjustable threshold from configuration for environment-specific calibration.
✅ Live Visualization: Uses Supervision annotators to draw skeleton edges and vertex points.
✅ Visual Alerts: Displays red bounding boxes and warning text when a fall is detected.
✅ Modular Design: Configurations and detection logic are cleanly separated for scalability.
✅ Lightweight and Fast: Efficient enough for edge devices and real-time applications.

⚙️ System Architecture

1. Video Input: Captured from webcam or video file.

2. Pose Detection: YOLO model detects human poses and outputs keypoints.

3. Keypoint Analysis: Algorithm compares ankle and body keypoints’ vertical distances.

4. Fall Decision: If average distance < threshold → fall detected.

5. Visualization: Annotates keypoints and displays fall alert overlay.

🛠️ Technologies Used

📌 Use Cases

• 🏥 Elderly Care Monitoring: Automatically detect falls in hospitals or homes.

• 🧑‍⚕️ Patient Safety Systems: Alert healthcare staff for immediate assistance.

• 🏠 Smart Home Safety: Integrated with surveillance cameras for senior safety.

• 🏭 Workplace Safety: Detect accidents or falls in industrial environments.

• 🧠 AI Research: Extendable to activity recognition or posture analysis tasks.

🔮 Possible Enhancements

• ☁️ Integration with IoT alert systems or SMS/email notifications.

• 📊 Logging fall events with timestamps to a database or CSV file.

• 🧩 Multi-person detection for monitoring multiple individuals simultaneously.

• 🧠 Deep learning–based posture classification for improved accuracy.

• 🕹️ Real-time dashboard using Streamlit or Flask web app.

✅ Conclusion

The Fall Detection System using YOLO and Pose Estimation provides a robust, real-time AI solution for monitoring human safety.
By combining deep learning pose detection with analytical keypoint logic, the system accurately detects and alerts potential falls.

This approach enhances safety monitoring in healthcare, homes, and workplaces — demonstrating the power of computer vision in real-world human safety applications.