Vitalytics

An AI-powered health and wellness Flutter application with on-device machine learning.

#Flutter#Machine Learning#HealthTech
Flutter
TensorFlow
SQLite
Node.js

Vitalytics is a holistic health and wellness companion. By seamlessly integrating fitness data, location tracking, and audio motivation, it provides an all-in-one ecosystem for users focused on improving their physical and mental well-being.

Timeline
2026
Team Size
2
Categories
Mobile App, HealthTech

Project Overview

The Problem

  • Health apps often offer fragmented experiences
  • Users are forced to switch between fitness trackers, mental wellness apps, and music players

Key Features

  • Native Health Bridge integration
  • On-Device AI classification
  • Deep Spotify/YouTube integration for context-aware background audio

Novelty

  • Runs complex machine learning models directly on the edge
  • Ensures zero-latency insights while completely preserving user privacy

Learning Outcome

  • Gained extensive experience in edge AI integration
  • Mastered bridging native device sensors with cross-platform frameworks

Detailed Features

Select a feature to learn more.

At the core of Vitalytics is a custom native bridge that securely accesses Apple Health and Google Fit data. This allows the app to pull real-time metrics such as heart rate, step count, and sleep analysis directly to local storage.

Unifying the health ecosystem

The Vision Behind Vitalytics

We wanted to create a platform that feels less like a sterile tracker and more like a personal coach. Vitalytics brings everything you need—music, metrics, and machine learning insights—into a single beautiful interface.

Paragraph media
Dashboard Overview

Dashboard Overview

Audio Controls

Audio Controls

Audio Controls

Audio Controls

Architecture Deep Dive

Building a feature-rich app like Vitalytics required careful architectural planning to ensure maintainability and performance. Utilizing Provider for state management and local SQLite databases ensures the app remains snappy even under heavy background loads.