EquiFall+

Landing Page
Web Cam
CCTV Live Monitoring
CCTV Dashboard
After Uploading the video
Staged Alert Protocol
Report
Mobile WebCam
Settings

Inspiration

Falls can happen in seconds, but help should never take minutes. EquiFall+ ensures someone is always watching, always ready, and always responding.

Falls are one of the leading causes of injury among elderly individuals, especially those living alone or in care facilities. In many cases, help doesn’t arrive quickly enough, not because people don’t care, but because no one knows a fall has happened.

What it does

EquiFall+ detects falls using video input from multiple sources and automatically alerts caregivers when a fall occurs. The system also stores incident data and provides analytics to help prevent future accidents. Key capabilities:

Detects falls from: Uploaded videos, Laptop webcams and CCTV/IP cameras
Automatically sends emergency alerts.
Stores incident history and replay footage.
Provides analytics and risk insights.
Tracks severity and response status.
Real-time monitoring dashboard.

How we built it

We built EquiFall+ using a modern AI-powered architecture:

Frontend: Lovable (React-based UI)
Backend: Python + FastAPI
Computer Vision: Fall detection using video frame analysis
Video Processing: OpenCV
API Communication: REST APIs
Deployment: Render
Camera Inputs: Webcam, CCTV and uploaded videos

The idea drew inspiration from one of our old projects, 'Person Detection Model' that used computer vision to track dynamic movement using binding boxes. This was adapted into an idea that assists a vulnerable section of the population.

Challenges we ran into

Handling multiple camera sources in one system.
Real-time fall detection without heavy compute resources.
Sending alerts without paid APIs (used simulated notifications).
Initially connecting frontend and backend dynamically using ngrok. Each challenge helped us improve the reliability and usability of our solution.

Accomplishments that we're proud of:

Built a fall detection platform.
Created a professional emergency monitoring dashboard.
Implemented incident replay and analytics.
Designed a scalable healthcare-ready UI.
Created a working end-to-end prototype.
Deploying backend on render.

What we learned

Real-time computer vision system design.
API-driven architecture.
UI design for emergency scenarios.
Handling camera streams and video processing.
Building scalable healthcare-focused applications.

What's next for Health Charm

Our vision is to make EquiFall+ a complete elderly safety platform for homes, hospitals, and care facilities. And we would to transform it into a mobile application that would make it user friendly.