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Fitness Tracker App with AI Coach

A cross-platform mobile app built with Flutter that helps users track workouts, monitor progress, and receive AI-powered guidance for exercise form correction and personalized fitness plans.

FlutterDartFirebase (Auth, Firestore, Storage)TensorFlow LiteGoogle Fit / HealthKit APIs
5 technologies
Updated 07/2023
Full-stack Application

Fitness Tracker App with AI Coach

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Project Architecture

Comprehensive overview of the project architecture, features, and development process with modern design patterns

Project Features

Discover the comprehensive feature set that powers this application

Core Features

User registration and profile management
Personalized workout plans based on user goals
Progress tracking for exercises, calories, and metrics
Role-based access for user and coach
Push notifications for reminders and achievements
Offline data support and synchronization

Operational Features

AI-based form correction using TensorFlow Lite
Video demonstration and exercise guidance
Integration with wearable devices (Google Fit / HealthKit)
Customizable workout schedules
Social sharing and leaderboard for motivation

Analytics & Reporting

Workout history and performance analytics
Progress visualization with charts and graphs
AI-based suggestions to improve form and intensity
Goal achievement tracking and notifications

System Architecture

Explore the technical foundation and architectural decisions

Frontend Architecture

Framework

Flutter

State Management

Provider / Riverpod

Charts Library

fl_chart for progress visualization

AI Integration

TensorFlow Lite for on-device form analysis

UI Libraries

Material DesignFlutter Widgets

Backend Architecture

Framework

Firebase Cloud Functions

Database

Firestore / Firebase Realtime Database

Authentication

Firebase Authentication (Email/Google/Facebook)

Real-time Communication

Firestore listeners for live progress updates

AI Services

Form Correction

TensorFlow Lite (Pose Estimation Models)

Workout Recommendation Engine

Rule-based and ML-enhanced suggestions

Development Phases

Strategic roadmap for project implementation and delivery

1

Project Setup & Authentication

1-2 weeks
Initialize Flutter project
Integrate Firebase for authentication and storage
Set up user profiles and role-based access
Create basic UI for login, registration, and dashboard
2

Workout Tracking & AI Integration

3 weeks
Develop workout tracking and progress modules
Integrate TensorFlow Lite for AI-based form correction
Implement exercise demonstration videos
Enable offline tracking with data sync
3

Dashboard & Analytics

2 weeks
Visualize user progress using charts and graphs
Implement AI suggestions for form and intensity improvement
Add goal tracking and achievement notifications
Integrate social sharing features
4

Testing & Optimization

1-2 weeks
Unit and integration testing for Flutter app
Test AI pose estimation accuracy
Optimize database reads and writes
Conduct end-to-end testing for user experience

API Endpoints

RESTful API structure and endpoint organization

Authentication

Register User

POST /api/auth/register

Login

POST /api/auth/login

Get User Profile

GET /api/auth/me

Workouts

Get Workout Plans

GET /api/workouts

Log Workout Session

POST /api/workouts/log

Get User Workout History

GET /api/workouts/history

AI Services

Analyze Exercise Form

POST /api/ai/form-analyze

Get AI Recommendations

GET /api/ai/recommendations

Notifications

Send Push Notification

POST /api/notifications/push

Testing Strategy

Comprehensive quality assurance and testing methodologies

Unit Testing

Flutter Test
Mockito

Integration Testing

Flutter Integration Test

End-to-End Testing

Appium
Cypress for Web dashboard if any

AI Testing

TensorFlow Lite model accuracy validation
Pose estimation and correction testing

Deployment Strategy

Production deployment and infrastructure management

Frontend

Google Play Store / Apple App Store

Backend

Firebase Cloud Functions

Database

Firestore / Realtime Database

CI/CD

GitHub Actions / Codemagic

Future Enhancements

Roadmap for upcoming features and improvements

Integration with smart wearables for heart rate and motion tracking
Voice-guided workouts and AI coaching
Gamification features and leaderboards
Personalized nutrition plans
Offline AI analysis for low-connectivity environments