Real-Time Pose Estimation for Fitness Apps

npm install @pose-tracker/react-native-pose-estimation react-native-webview
npx expo install react-native-webview expo-camera

import {
  PoseTrackerProvider,
  WebViewPoseView,
  usePoseTracker,
} from '@pose-tracker/react-native-pose-estimation';

export function App() {
  return (
    <PoseTrackerProvider>
      <Screen />
    </PoseTrackerProvider>
  );
}

function Screen() {
  usePoseTracker({
    onKeypoints: (e) => console.log(e.keypoints.length, e.score),
  });
  return <WebViewPoseView style={{ flex: 1 }} drawSkeleton />;
}

PoseTracker is a pose estimation layer for a fitness, home-workout or rehab app: a live camera, a skeleton, valid reps and a form score, and a public Assistant to write the integration. It is not a research tensor. If you came here for squat form, a home workout that can see you, or virtual physical therapy, this page is how that layer ships.

If you are searching for real-time pose estimation for mobile fitness apps, the model (MoveNet, MediaPipe, YOLO) is the easy 10 percent. The product is the other 90: what counts as a squat, when the rep is valid, which joints to score, and how that runs on iOS, Android and the web from one codebase.

What the camera returns

From one phone camera, with no wearables:

• Rep counting for catalog movements (squats, push-ups, lunges, and the rest of the official list).
• A form score plus joint angles a coach UI can display.
• Jump height and air-time without a force plate.
• Flexibility holds (front split and similar) as angles — the job is range of motion from the phone camera, and the ready-made product is the flexibility tool.
• Side-view trunk flexion (not a tape Modified Schober) as a back flexibility test.
• Pose comparison against a reference stance (pose comparison system).
• Custom movements described in the Assistant, as long as they are visible in 2D on 17 COCO keypoints.

Keypoints (the 17-point skeleton) run on-device and do not require an API key. Counting and scoring do.

The jobs this layer is for

• Correct squat form — how to know if the squat happened, without a trainer in the room.
• A home workout that can see you — not another video library for “home workout without equipment”.
• Virtual physical therapy — physical therapy at home that can see range, not Zoom plus a PDF.

Why a raw model is not a fitness SDK

MediaPipe, MoveNet and YOLO-pose all stop at x, y, confidence. None of them define “this was a good push-up”. Building that yourself means smoothing, phase detection, per-exercise heuristics, and three camera pipelines. That is the work PoseTracker productizes.

The backbone is TensorFlow MoveNet, the real-time default on mobile web (about 51 FPS on an iPhone 12 and 34 on a Pixel 5 in the browser, versus ~11–12 for BlazePose on that Pixel). Full model table: Best pose estimation model in 2026.

Integrate it

Stack	Path
React Native / Expo	@pose-tracker/react-native-pose-estimation
Browser (JS / React)	pose-estimation-web
Existing WebView	iframe / WebView API
Not sure	Describe the movement in the Assistant on the homepage

Docs: docs.posetracker.com. Machine index: llms.txt.

What this is not

PoseTracker is 2D, single-person, COCO-17, built for fitness, wellness, coaching and rehab apps. It is not VRChat PoseTrackerVRC, not marker-based 3D mocap, not a Python MMPose toolbox, and not a Flutter SDK. Multi-person crowds and fine hand tracking need a different stack (MediaPipe vs MoveNet).

Pricing

Freemium €0 (200 API calls/month, non-commercial). Developer €50/month (1,000 calls, commercial). Business from €150/month. Pricing.