melihuzunoglu/human-fall-detection

Human Fall Detection with YOLOv11

This model is a specialized version of YOLOv11, fine-tuned to detect human falls in various environments. It is designed to provide real-time alerts for safety monitoring in elderly care facilities, hospitals, and industrial workplaces.

🚀 Quick Start (Usage)

You don't need to download the weights manually. You can load and run the model directly using the Python code below:

from ultralytics import YOLO
from huggingface_hub import hf_hub_download
import os

model_path = hf_hub_download(repo_id="melihuzunoglu/human-fall-detection", filename="best.pt")

model = YOLO(model_path)
 
results = model.predict(source="image1.jpg", conf=0.25, save=True)

✅ Supported Classes (Labels)

The model can detect and distinguish between the following three states:

Fallen: Active falling motion or a person on the ground after a fall.

Sitting: People sitting on chairs, benches, or floor.

Standing: People in an upright, standing position.

📊 Model Information

Architecture: YOLOv11 (Ultralytics)

Task: Object Detection (Fall Detection)

Input Resolution: 640x640 pixels

Inference Speed: Optimized for real-time applications

🎯 Target Applications

Elderly Safety: Automated fall detection for home or nursing home environments.

Occupational Health: Monitoring falls in hazardous work zones or construction sites.

Healthcare Support: Providing an extra layer of monitoring for patient rooms.

🛠 Training Details

The model was trained using the Ultralytics framework. The dataset was curated and pre-processed via Roboflow to ensure high accuracy and minimal false positives in common sitting or lying down scenarios.

👤 Developer

Author: Melih Uzunoğlu Linkedin

Framework: Ultralytics YOLOv11

Dataset Source: Roboflow

Disclaimer

This model is developed for educational and research purposes. For critical safety implementations, it should be integrated with professional-grade monitoring systems.