Overview
Child Fall Detection System is an AI-powered safety monitoring solution that detects child falls in real time using pose estimation, edge-friendly computer vision, and instant alert delivery through IoT communication pipelines.
Problem to Solve
Build a low-latency and reliable child safety monitoring system that can automatically detect fall incidents and notify caregivers immediately, while remaining affordable, scalable, and practical for home or daycare deployment.
Solution Approach
Developed an AI-based fall detection pipeline using MediaPipe pose estimation to identify body movement patterns associated with child falls. The vision system was integrated with ESP32-CAM for real-time image capture and MQTT for lightweight communication between edge devices and backend services.
An instant alert mechanism was implemented through the Telegram Bot API, enabling caregivers to receive immediate notifications whenever a fall event is detected. Edge computing principles were applied to improve responsiveness, reduce latency, and support efficient operation in resource-constrained environments.
Key Technologies
- Python
- MediaPipe
- Computer Vision
- ESP32-CAM
- MQTT
- Telegram Bot API
- Edge Computing
- IoT Systems