Our ergonomic tracking system is powered by a creative tech stack that seamlessly integrates sensor data and presents it in an intuitive, user-friendly interface. Below is a detailed overview of each compoent within the system architecture:
| Layer | Technology | Purpose |
|---|---|---|
| Hardware Sensor Layer | Leap Motion Controller | Captures high-fidelity 3D hand, wrist, and finger movement data at 120 frames per second using infrared imaging. |
| Sensor Processing Layer | Leap Motion Service (Desktop) | Processes raw infrared data into structured 3D hand models with positional and rotational information. |
| Backend Computation Layer | Python + NumPy + WolframAlpha API | Extracts positional data, computes joint angles and positions. |
| Database Layer | Firebase Firestore | Stores user posture metrics, timestamps, and ergonomic analytics in a real-time cloud database. |
| Frontend Application Layer | React.js (Vite or Next.js) | Displays real-time ergonomic feedback, posture alerts, and diagnostic reports through an interactive web interface. |
| Frontend Data Fetching Layer | Firebase Web SDK | Fetches real-time posture data from Firestore and pushes live updates to the React frontend interface. |
| Styling and UI Layer | TailwindCSS / shadcn/ui | Applies responsive, mobile-first design principles to ensure a smooth and readable user experience across devices. |
| Hosting Layer (Optional) | Vercel or Firebase Hosting | Hosts the web application and ensures low-latency delivery of the dashboard to users globally. |
The Leap Motion sensor continuously captures hand position data, which is processed by our Python backend to detect ergonomic risks. This data is then stored in a Firestore Database and immediately reflected in the React frontend, providing users with real-time feedback about their hand posture and potential strain risks.
© het.ai | Product of HackTech '25
Benjamin Garcia
Russell Soo
Jonathan Soo
Katelyn Teav