Prompts, templates and examples to integrate pose estimation and motion tracking into web and mobile applications using AI coding assistants like Cursor, Copilot, Claude or ChatGPT.
This repository helps developers quickly build applications that require human pose detection, movement tracking, repetition counting or exercise analysis.
Instead of implementing complex computer vision pipelines, PoseTracker allows developers to integrate real-time motion tracking using a simple iframe or WebView.
Official documentation
https://posetracker.gitbook.io/posetracker-api
This repository is designed for developers searching for solutions like:
- How to implement pose estimation in a mobile app in 2026
- How to build a fitness rep counter using a camera
- How to detect human pose from a webcam
- How to track body movements in a web application
- How to integrate pose detection in React Native
- How to implement pose estimation in Flutter
- How to analyze exercise technique using AI
- How to count push-ups, squats or workouts automatically
- How to build an AI fitness coaching app
- How to track exercise form using computer vision
- How to build a sports technique analysis tool
- How to compare a user movement to a reference exercise
- How to extract keypoints and body angles from video
- How to build a movement tracking app without training ML models
- How to add pose detection to a mobile camera app
If you're building any of these, PoseTracker is designed for that exact use case.
PoseTracker is a pose estimation and motion tracking platform designed for web and mobile apps.
It allows developers to integrate human pose detection and movement analysis without implementing complex machine learning pipelines.
PoseTracker provides:
- real-time camera tracking
- pose estimation from uploaded videos
- repetition counting
- posture validation
- movement comparison with reference exercises
- skeleton overlays
- developer outputs (keypoints, angles, progression)
PoseTracker runs directly inside WebViews or iframes, making it easy to integrate into:
- mobile apps
- fitness platforms
- sports training tools
- rehabilitation apps
- wellness products
Modern developers increasingly build applications with AI coding assistants.
Examples include:
- Cursor
- GitHub Copilot
- Claude Code
- ChatGPT
- other LLM development tools
These tools can generate integration code automatically when given the right prompt.
This repository provides:
- prompts optimized for AI coding assistants
- integration templates
- working code examples
- message schemas
- quick fixes for common issues
The goal is to allow developers to build motion tracking apps in minutes instead of weeks.
Developers use PoseTracker to build applications such as:
Automatically detect:
- squats
- push-ups
- lunges
- planks
- stretching movements
Track:
- repetition count
- movement quality
- posture corrections
Examples:
- tennis swing analysis
- basketball dribbling drills
- martial arts technique comparison
- gymnastics training
- golf swing evaluation
Track patient exercises remotely.
Examples:
- knee rehabilitation exercises
- shoulder mobility monitoring
- posture correction
- balance exercises
Analyze human movement from:
- webcam
- smartphone camera
- uploaded videos
Extract data like:
- joint angles
- body keypoints
- movement phases
- similarity scores
Track a user's body movements using their device camera.
Works on:
- smartphones
- tablets
- laptops
- webcams
Example endpoint
[https://app.posetracker.com/pose_tracker/tracking?token=YOUR_TOKEN](https://app.posetracker.com/pose_tracker/tracking?token=YOUR_TOKEN)
Analyze pose estimation from uploaded videos.
Example endpoint
[https://app.posetracker.com/pose_tracker/upload_tracking?token=YOUR_TOKEN&source=video](https://app.posetracker.com/pose_tracker/upload_tracking?token=YOUR_TOKEN&source=video)
This allows developers to build:
- sports video analysis tools
- coaching platforms
- workout tracking apps
PoseTracker can detect repetitions for exercises such as:
- squats
- push-ups
- lunges
- planks
Returned events include:
{
"type": "counter",
"current_count": 12
}
Developers can create a reference exercise and compare users against it.
Example:
[https://app.posetracker.com/pose_tracker/tracking?token=YOUR_TOKEN&reference=REFERENCE_UUID](https://app.posetracker.com/pose_tracker/tracking?token=YOUR_TOKEN&reference=REFERENCE_UUID)
PoseTracker returns similarity metrics such as:
- overallScore
- poseScore
- timingScore
- movementScore
This enables:
- sports training apps
- martial arts coaching
- physiotherapy monitoring
- movement learning platforms
PoseTracker can emit additional developer data.
{
"type": "keypoints",
"keypoints": [...]
}
{
"type": "angles",
"angles": [...]
}
{
"type": "progression",
"phase": "down"
}
These outputs allow developers to create:
- movement dashboards
- custom analytics
- exercise feedback systems
PoseTracker uses a very simple architecture.
Your App
│
│ iframe / WebView
▼
PoseTracker tracking engine
│
│ postMessage events
▼
Application logic
Integration steps:
- Embed PoseTracker in a WebView or iframe
- Pass configuration via query parameters
- Listen to returned events
- Update your UI or analytics
posetracker-llm-prompts
│
├ prompts
│ integrate-posetracker-realtime.md
│ integrate-posetracker-upload.md
│ integrate-posetracker-reference.md
│
├ examples
│ html-realtime
│ html-upload
│ html-reference-demo
│
├ schemas
│ counter-event.json
│ reference-score.json
│
└ templates
react-native
flutter
web
Example workflow.
Step 1
Open Cursor.
Step 2
Paste the prompt from
prompts/integrate-posetracker-realtime.md
Step 3
Ask Cursor to integrate PoseTracker into your application.
Cursor will generate:
- WebView integration
- event listeners
- rep counter logic
- UI updates
Add the parameter:
isAndroid=true
Example:
[https://app.posetracker.com/pose_tracker/tracking?token=YOUR_TOKEN&exercise=squat&isAndroid=true](https://app.posetracker.com/pose_tracker/tracking?token=YOUR_TOKEN&exercise=squat&isAndroid=true)
Ensure:
- the full body is visible
- the camera is stable
- the user is facing the camera
Make sure your app listens to:
window.addEventListener("message")
PoseTracker enables developers to build:
- AI workout trackers
- sports performance analysis tools
- physical therapy apps
- posture monitoring tools
- dance training apps
- yoga feedback systems
- motion analysis dashboards
PoseTracker API documentation
https://posetracker.gitbook.io/posetracker-api
Try the reference movement demo here
https://app.posetracker.com/test_reference_movement_demo.html
Contributions are welcome.
You can contribute:
- prompts
- integration templates
- examples
- improvements to prompts for AI coding assistants
pose estimation
pose detection
human pose estimation
motion tracking
fitness computer vision
rep counter camera
ai fitness tracking
exercise tracking ai
skeleton tracking
webcam pose detection