🧠 E.C.H.O. – Emergency Cognitive Health Observer

AI-powered real-time distress detection system using audio intelligence

E.C.H.O. (Emergency Cognitive Health Observer) is an intelligent audio-based AI system that continuously monitors environmental sounds to detect distress signals, anomalies, and emergency situations.

It leverages machine learning + audio signal processing to act as a smart auditory safety assistant in real time.

🎯 Problem Statement

In many real-world scenarios (homes, hospitals, public places), distress signals go unnoticed due to lack of active monitoring.

❗ Screams, cries, or panic sounds often don't trigger immediate help.

E.C.H.O. solves this by:

Listening continuously 👂 Understanding sound patterns 🧠 Triggering alerts instantly 🚨 🚀 Key Features 🎙️ Real-time audio capture 🤖 AI-based sound classification 🚨 Distress detection (screams, crying, panic sounds) 📊 Confidence scoring ⚡ Lightweight and fast processing 🔔 Alert triggering system 🧠 Scalable AI architecture 🏗️ System Architecture 🎤 Microphone Input ↓ 🔧 Audio Preprocessing ↓ 📊 Feature Extraction (MFCC / Spectrogram) ↓ 🤖 ML Model (Sound Classification) ↓ ⚠️ Distress Detection Engine ↓ 🚨 Alert System (Notification / Trigger) 🧪 Demo (Add your demo here)

🔗 Demo Video: (Add YouTube / Drive link) 📸 Screenshots:

Real-time detection Alert trigger system

(Tip: Upload screenshots/GIFs later for maximum impact)

📂 Project Structure ECHO/ │── models/ # Trained ML models │── data/ # Audio dataset │── src/ │ ├── audio_capture.py │ ├── preprocessing.py │ ├── feature_extraction.py │ ├── model.py │ ├── detection.py │── alerts/ # Alert logic │── main.py │── requirements.txt │── README.md 🛠️ Tech Stack 💻 Languages Python 📚 Libraries & Frameworks Librosa (audio processing) NumPy PyAudio Scikit-learn / TensorFlow / PyTorch ⚙️ Tools Git & GitHub VS Code ⚙️ Installation

git clone https://github.com/jinendrabanthia/ECHO.git

cd ECHO

pip install -r requirements.txt ▶️ Usage python main.py 🧾 Example Output 🎧 Listening... Detected Sound: Screaming Confidence: 92% ⚠️ ALERT: Possible distress detected! 📊 Use Cases 🏥 Hospital patient monitoring 👴 Elderly care & fall detection 🏠 Smart home safety 🛡️ Security & surveillance 🚨 Emergency detection systems 🔮 Future Scope 📱 Mobile app integration ☁️ Cloud-based monitoring 📡 IoT device integration 🔔 SMS / WhatsApp alerts 🎯 Higher accuracy with deep learning models 🌍 Multi-language & environment adaptability 🤝 Contributing

Contributions are welcome!

git checkout -b feature-name

git commit -m "Added feature"

git push origin feature-name

Then open a Pull Request 🚀

📜 License

This project is licensed under the MIT License.

👨‍💻 Author

Jinendra Banthia 💼 Backend Developer | Data Science Enthusiast

🔗 GitHub: https://github.com/jinendrabanthia

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“E.C.H.O. doesn’t just hear — it understands.”