1.Abstract

The rapid growth of Artificial Intelligence (AI) and computer vision technologies has created advanced solutions for healthcare monitoring and elderly safety systems. This project, titled “AI-Based Fall Detection System,” is designed to automatically detect human fall incidents and send emergency alerts to caretakers in real time. The main objective of the system is to provide immediate assistance to elderly people or individuals living alone who may experience accidental falls inside their homes or healthcare environments.

Falls are one of the major health risks among elderly individuals and can lead to severe injuries, delayed medical support, and life-threatening situations if immediate help is not available. Although surveillance cameras are widely used for monitoring, continuous human observation is difficult and impractical. To overcome this problem, the proposed system uses Artificial Intelligence to automatically analyze video feeds, identify human falls, and trigger alert notifications without human intervention.

The system combines advanced object detection and pose estimation techniques for accurate fall analysis. Initially, the YOLOv8 model is used to detect and track the presence of a person in the video frame. After detecting the person, the MediaPipe library is used to identify human body landmarks and skeletal joints. By analyzing body posture, movement direction, and joint orientation, the system determines whether a fall-down event has occurred.Once a fall is detected, the system automatically sends an alert notification to the caretaker or concerned user using the MQTT Protocol. The notification mechanism ensures rapid communication and immediate awareness of emergency situations. The project also uses the Flask framework to develop a simple and interactive web-based interface for monitoring system activity and displaying detection results.

The proposed system offers several advantages over traditional monitoring systems. It provides continuous automated monitoring, reduces the need for manual supervision, improves response time during emergencies, and enhances the safety of elderly individuals. The use of AI-based video analysis increases detection accuracy and minimizes false alerts by analyzing real human movement patterns instead of relying solely on motion sensors.

Overall, this project demonstrates the practical application of Artificial Intelligence, computer vision, and IoT communication in healthcare and safety monitoring systems. The AI-Based Fall Detection System provides an efficient, intelligent, and real-time solution for detecting fall incidents and ensuring timely assistance, thereby improving the quality of life and safety of elderly people and vulnerable individuals.

2. Objectives

1. To detect human presence in real-time using AI models.
2. To identify fall incidents using pose estimation techniques.
3. To send instant alerts to caregivers during emergencies.
4. To ensure continuous monitoring of elderly individuals.
5. To reduce response time during critical situations.

3. Existing System

• Traditional fall detection systems mainly depend on wearable sensors such as accelerometers and gyroscopes to monitor body movements.

• Some existing systems use surveillance cameras with basic motion detection techniques to identify abnormal activities.

• Manual monitoring through CCTV cameras requires continuous human supervision, which is difficult to maintain 24/7.

• Certain AI-based systems can detect human movement, but they often fail to accurately differentiate between normal activities and actual fall incidents.

• Existing systems may produce false alarms due to environmental conditions, lighting variations, or complex backgrounds.

4. Proposed System

• Develop an AI-based fall detection system that automatically detects human falls using video input.

• Use YOLOv8 to detect and track humans in video streams.

• Implement MediaPipe to analyze human body posture and skeletal joints.

• Detect fall incidents by analyzing body orientation, movement direction, and posture changes.

• Use the MQTT Protocol to send emergency notifications to caretakers.

• Develop a web-based monitoring interface using Flask for real-time monitoring and visualization.

5. Implementation Procedure

1. Install required libraries:
2. OpenCV
3. MediaPipe
4. Ultralytics YOLOv8
5. Flask
6. MQTT client (paho-mqtt)
7. Capture real-time video feed from camera.
8. Detect human presence using YOLOv8 model.
9. Extract body landmarks using MediaPipe Pose.
10. Analyze posture changes and angle thresholds.
11. Detect sudden vertical-to-horizontal movement (fall detection logic).
12. Trigger alert if fall is confirmed.
13. Send notification via MQTT to caregiver system.
14. Display live monitoring results using Flask web interface.

6.Software Requirements

• Operating System                    : Windows 10 / 11

• Programming Language          : Python

• Front End                                 : HTML, CSS

• Framework                               : Flask

• Libraries : OpenCV, YOLOv8, MediaPipe

• Communication Protocol         : MQTT

• Server : XAMPP / Local Server

7.Hardware Requirements

• Processor                                  : Intel i3 or above

• RAM                                        : 4 GB or above

• Hard Disk                                 : 500 GB

• Camera                                     : Webcam / CCTV Camera

• Internet Connection                 : Required for MQTT notification service

8. Advantages of the Project

1. Real-time fall detection and alert system
2. Improves safety of elderly people living alone
3. Reduces dependency on continuous human monitoring
4. Fast emergency response through instant notifications
5. Works with existing CCTV/camera systems
6. Scalable for hospitals and smart homes
7. Combines AI + IoT for smart healthcare solution