Case Study: AlignMate - A Smart Approach to Posture Correction

The Challenge: Combating Poor Posture in the Digital Age

As part of a thesis project for the Polytechnic University of the Philippines, the goal was to create an innovative and engaging solution to the growing problem of poor posture. The primary challenge was to build a system that could provide real-time, personalized feedback without using intrusive cameras, relying instead on a custom hardware sensor.

My Solution: An Ecosystem for Posture Improvement

I developed AlignMate, a comprehensive system that pairs a custom-built Inertial Measurement Unit (IMU) sensor with a machine learning model and a feature-rich web application. The platform is a complete ecosystem designed for effective posture correction.

• Real-Time Posture Monitoring: The system provides live data streaming from the hardware sensor, offering visual feedback and dynamic notifications to alert users of poor posture.
• Intelligent Calibration System: A guided, multi-step process trains a personalized machine learning model for each user, ensuring accurate and tailored feedback.
• Interactive Dashboard: An interactive dashboard displays aggregated posture data, daily performance summaries, and 7-day trends, allowing users to track their progress over time.
• Gamified Achievements & Progression: To keep users motivated, the system includes gamified elements where users can earn points and rewards across various themes like 'Pet Care' or 'City Builder'.
• In-Depth Analysis & Reporting: The system provides detailed analysis, personalized recommendations, and the ability to export daily reports as PDFs for a comprehensive overview of posture data.

A Multi-Faceted Technology Stack

This project integrated a modern web frontend with a real-time backend to deliver a seamless user experience:

• Frontend: React.js for building a dynamic user interface, with React Router for navigation.
• Backend & Database: Google Firebase for its Realtime Database, user authentication, and hosting services.
• Data Visualization: `react-native-chart-kit` was used to create the various charts and graphs on the dashboard.
• PDF Generation: `jsPDF` and `jspdf-autotable` were implemented to allow users to export their data reports.

Interested in integrating IoT or machine learning into your next project? Get in touch!