SwipeHealth

Description

SwipeHealth reimagines fitness coaching with AI that understands the nuances of individual fitness journeys. Instead of following a generic program, users get a personalized coach that adapts based on their responses. The system generates complete workout programs tailored to fitness level, equipment access, and specific goals. It understands injury history and creates modification-aware programs; users with shoulder injuries get exercises that protect rather than strain the shoulder. The app tracks performance and automatically adjusts progression; if an exercise becomes too easy, the next workout increases difficulty. It respects recovery; if users log poor sleep or fatigue, the app scales back intensity. Scheduling is flexible; if a user misses a day, the program adapts rather than requiring them to catch up. The result is a genuinely personalized fitness experience that meets users where they are.

Key Features

Personalized Workout Generation: AI creates complete workout programs based on fitness level, goals, equipment, and preferences; variety to prevent boredom; science-backed exercise selection.
Injury-Aware Programming: Programs account for past injuries and limitations; exercises chosen to work around restrictions; modifications for common issues like shoulder, knee, and back pain.
Exercise Progression: Performance tracking automatically adjusts difficulty; exercises progress when users are ready; prevents overuse injuries from doing too much too soon.
Adaptive Training Schedules: Training frequency adapts based on recovery data; users log sleep, soreness, and energy; app scales intensity and volume accordingly.
Real-Time Form Coaching: Video analysis of exercise form with real-time correction; AI coaches users through exercises; prevents injury from poor technique.
Performance Tracking: Tracks all workouts, exercises, sets, reps, and weight used; visualizes progress over time; identifies plateaus and helps break through them.
Nutrition Integration: Basic nutrition guidance tailored to fitness goals; meal logging and macro tracking; integration with other fitness apps.
Community & Accountability: Social features for sharing progress; challenges with other users; leaderboards for motivation; accountability through community.

Technical Highlights

Personalization Engine: Machine learning models that understand user fitness level, capabilities, and limitations; generates programs tailored to the individual, not the average.
Form Analysis: Computer vision for analyzing exercise form from phone camera; real-time feedback on form corrections; prevents injury from poor technique.
Progression Algorithm: Models that predict when users are ready to progress; balances challenge with risk of injury; data-driven progression recommendations.
Recovery Analysis: Integration with wearables for sleep, heart rate variability, and recovery metrics; models that use recovery data to adjust training intensity.
Injury Knowledge Base: Comprehensive database of exercises with modifications for common injuries; contraindications for certain movements based on injury history.
Performance Analytics: Tracking of all workouts and performance trends; predictive models for plateaus and optimal progression paths.
Real-Time Coaching: OpenAI integration for conversational coaching; users ask questions and get personalized answers; motivation and guidance throughout workouts.

Tech Stack

Mobile App

React Native for iOS and Android
TypeScript for type-safe code
Camera integration for form analysis
Wearable SDKs for health data integration

Backend Services

Node.js and TypeScript
Express for REST API
PostgreSQL for workout data and user profiles
Redis for caching and real-time updates

AI & Machine Learning

OpenAI API for coaching and guidance
Computer vision models for form analysis
Machine learning models for progression and recovery
TensorFlow for on-device inference

Health Data Integration

Apple HealthKit and Google Fit integration
Wearable API integrations (Fitbit, Oura, etc.)
Heart rate and sleep data ingestion

Infrastructure

AWS for compute and storage
S3 for video storage and form analysis
Lambda for video processing
CloudWatch for monitoring

Analytics

User engagement tracking
Workout completion and consistency metrics
Progress and results tracking
Retention and churn analysis

Description

Key Features

Personalized Workout Generation: AI creates complete workout programs based on fitness level, goals, equipment, and preferences; variety to prevent boredom; science-backed exercise selection.

Injury-Aware Programming: Programs account for past injuries and limitations; exercises chosen to work around restrictions; modifications for common issues like shoulder, knee, and back pain.

Exercise Progression: Performance tracking automatically adjusts difficulty; exercises progress when users are ready; prevents overuse injuries from doing too much too soon.

Adaptive Training Schedules: Training frequency adapts based on recovery data; users log sleep, soreness, and energy; app scales intensity and volume accordingly.

Real-Time Form Coaching: Video analysis of exercise form with real-time correction; AI coaches users through exercises; prevents injury from poor technique.

Performance Tracking: Tracks all workouts, exercises, sets, reps, and weight used; visualizes progress over time; identifies plateaus and helps break through them.

Nutrition Integration: Basic nutrition guidance tailored to fitness goals; meal logging and macro tracking; integration with other fitness apps.

Community & Accountability: Social features for sharing progress; challenges with other users; leaderboards for motivation; accountability through community.

Technical Highlights

Personalization Engine: Machine learning models that understand user fitness level, capabilities, and limitations; generates programs tailored to the individual, not the average.

Form Analysis: Computer vision for analyzing exercise form from phone camera; real-time feedback on form corrections; prevents injury from poor technique.

Progression Algorithm: Models that predict when users are ready to progress; balances challenge with risk of injury; data-driven progression recommendations.

Recovery Analysis: Integration with wearables for sleep, heart rate variability, and recovery metrics; models that use recovery data to adjust training intensity.

Injury Knowledge Base: Comprehensive database of exercises with modifications for common injuries; contraindications for certain movements based on injury history.

Performance Analytics: Tracking of all workouts and performance trends; predictive models for plateaus and optimal progression paths.

Real-Time Coaching: OpenAI integration for conversational coaching; users ask questions and get personalized answers; motivation and guidance throughout workouts.

Tech Stack

Mobile App

React Native for iOS and Android

TypeScript for type-safe code

Camera integration for form analysis

Wearable SDKs for health data integration

Backend Services

Node.js and TypeScript

Express for REST API

PostgreSQL for workout data and user profiles

Redis for caching and real-time updates

AI & Machine Learning

OpenAI API for coaching and guidance

Computer vision models for form analysis

Machine learning models for progression and recovery

TensorFlow for on-device inference

Health Data Integration

Apple HealthKit and Google Fit integration

Wearable API integrations (Fitbit, Oura, etc.)

Heart rate and sleep data ingestion

Infrastructure

AWS for compute and storage

S3 for video storage and form analysis

Lambda for video processing

CloudWatch for monitoring

Analytics

User engagement tracking

Workout completion and consistency metrics

Progress and results tracking

Retention and churn analysis

Command palette