# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 4 System Implementation
### Technologies and Libraries Used  
The development of this fall detection system relies on a combination of Python libraries, each contributing to specific functionalities such as posture detection, fall recognition, user interaction, and emergency alerting. The system's architecture brings together both computer vision and voice processing tools to create a robust, real-time monitoring solution.  
At the core of the visual processing component lies OpenCV, an open-source computer vision library used to access the webcam feed, capture video frames in real time, and render visual feedback such as body keypoints, skeletal overlays, and posture labels directly  
Figure 4: Real-time fall detection and alert workflow.  
on the video stream. To complement this, NumPy is employed for efficient numerical computations, especially when handling vectors and matrices associated with body pose data. It facilitates keypoint processing, angle calculations, and classification of postures by analyzing spatial relationships among detected body parts.  
The backbone of the fall detection model is the YOLOv11-pose architecture, accessed through the Ultralytics API. This model enables fast and accurate pose estimation, serving as the foundation for recognizing abnormal movements and potential fall events.  
For voice interaction, the system utilizes pyttsx3, a text-to-speech synthesis library that operates on-device locally, enabling the platform to deliver vocal alerts when a fall is detected. It also engages the user by asking for verbal feedback regarding their condition. In parallel, the speech_recognition library captures audio input via the microphone and processes it using Google's speech recognition API. This allows the system to interpret spoken responses such as \"I'm okay\" or \"I need help,\" determining the appropriate course of action based on the user's reply. These functions have already been validated and tested under suitable conditions, and for this development phase, we decided to use them in their predefined form without further modifications or enhancements.  
To manage asynchronous operations, the system employs asyncio, which ensures that tasks like sending messages do not interfere with ongoing video processing. Similarly, the threading module allows various components, such as real-time image analysis and background communications, to run in parallel, maintaining smooth and responsive performance.  
Finally, the system integrates with the Telegram messaging platform through the python-telegram-bot library. This enables the system to send immediate alerts to caregivers when a fall is confirmed and assistance is requested, supporting remote supervision and timely intervention.  
Together, these libraries form a cohesive and efficient software stack that supports the system's multimodal functionality, real-time responsiveness, and user-focused interaction.  
Figure 5 illustrates the automatic alert message sent via the Telegram API when a fall is detected and the user fails to respond. The message provides concise, actionable information to caregivers in real time.