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---

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# 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:

```python
from ultralytics import YOLO

# 1. Load the model from Hugging Face
model = YOLO("melihuzunoglu/human-fall-detection")

# 2. Run inference on an image or video
# Replace 'your_video.mp4' with your actual file path
results = model.predict(source="your_video.mp4", conf=0.25, save=True)

# 3. View results
for result in results:
    result.show()
```

## ✅ Supported Classes (Labels)

The model can detect and distinguish between the following three states:
```python
Falling: 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
```python
Architecture: YOLOv11 (Ultralytics)

Task: Object Detection (Fall Detection)

Input Resolution: 640x640 pixels

Inference Speed: Optimized for real-time applications
```

## 🎯 Target Applications
```python
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](https://www.linkedin.com/in/melih-uzunoglu/)

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.