Stop Slouching! Build an AI-Powered Posture Monitor with MediaPipe & Electron 🚀

Source: Dev.to

Are you reading this while hunched over your keyboard like a gargoyle? 🧙‍♂️ Don’t worry, we’ve all been there. Tech Neck is the silent productivity killer of the 21st century. But as developers, we don’t just complain about back pain—we code solutions for it.

In this tutorial we will build a Real‑time Posture Correction System. By leveraging Real‑time Pose Estimation, MediaPipe, and WebRTC, we’ll create a desktop application that watches your back (literally) and sends a system‑level notification when your posture starts to slip. We’ll use JavaScript Computer Vision to turn your webcam into a high‑tech ergonomic consultant.

If you are looking for more advanced, production‑ready AI patterns and deep dives into vision models, check out the official WellAlly Tech Blog for more inspiration!

🏗 The Architecture

Before we dive into the code, let’s look at how the data flows from your webcam to your nervous system (via a notification).

graph TD
    A[Webcam / WebRTC] -->|Raw Video Stream| B(MediaPipe Pose Engine)
    B -->|Landmark Coordinates| C{Geometry Logic}
    C -->|Angle > Threshold| D[Slouch Detected]
    C -->|Angle |IPC Communication| F[Electron Main Process]
    F -->|System Notification| G[User's Desktop]
    G -->|User Sits Up Straight| A

🛠 Prerequisites

To follow along, you’ll need a basic understanding of JavaScript. Here is our “Anti‑Slouch” tech stack:

• MediaPipe – Google’s powerhouse for cross‑platform ML solutions.
• TensorFlow.js – Run models directly in the browser/Electron.
• WebRTC – Handles the camera stream.
• Electron – Wraps our app into a desktop tool that can send system notifications.

👨‍💻 Step 1: Setting up the Camera Stream (WebRTC)

First, we need to grab the video feed. Using the getUserMedia API is the standard way to pipe video into our ML model.

// renderer.js
async function setupCamera() {
    const video = document.getElementById('webcam');
    const stream = await navigator.mediaDevices.getUserMedia({
        video: { width: 640, height: 480 },
        audio: false,
    });
    video.srcObject = stream;

    return new Promise((resolve) => {
        video.onloadedmetadata = () => resolve(video);
    });
}

🧠 Step 2: Detecting the Skeleton with MediaPipe

MediaPipe’s Pose model provides 33 3D landmarks. For posture we specifically care about the Nose (0) and the Shoulders (11, 12).

import { Pose } from "@mediapipe/pose";

const pose = new Pose({
  locateFile: (file) =>
    `https://cdn.jsdelivr.net/npm/@mediapipe/pose/${file}`,
});

pose.setOptions({
  modelComplexity: 1,
  smoothLandmarks: true,
  minDetectionConfidence: 0.5,
  minTrackingConfidence: 0.5,
});

pose.onResults(onResults); // We'll define the logic here

📐 Step 3: The “Slouch Logic” (Geometric Calculation)

How do we define a “slouch”? Usually it’s when the vertical distance between the nose and the shoulders becomes too small, indicating the head is leaning forward.

function calculateSlouch(results) {
    const landmarks = results.poseLandmarks;
    if (!landmarks) return;

    // Grab the nose and the average shoulder position
    const nose = landmarks[0];
    const leftShoulder = landmarks[11];
    const rightShoulder = landmarks[12];

    const shoulderY = (leftShoulder.y + rightShoulder.y) / 2;
    const neckDistance = shoulderY - nose.y;

    // If the vertical distance drops below a threshold,
    // the user is likely slouching.
    const THRESHOLD = 0.15;

    if (neckDistance  60_000) {
        window.electronAPI.sendAlert(); // IPC to main process
        lastAlertTime = Date.now();
    }
}

🌟 The “Official” Way to Scale

While this beginner‑friendly version works locally, production‑grade vision apps need more robust handling—e.g., varying lighting conditions, camera angles, and model quantization for performance.

For deep‑dive architectural patterns on optimizing MediaPipe for production or integrating large multimodal models (like GPT‑4o) for advanced visual reasoning, explore the resources at wellally.tech/blog. They cover everything from edge deployment to sophisticated vision pipelines that go far beyond simple coordinate math. 🥑

🎯 Conclusion

You’ve just built a functional AI assistant that protects your spine! 🦴 Here’s what we covered:

• Capturing video via WebRTC.
• Extracting human landmarks with MediaPipe.
• Calculating a slouch metric using simple geometry.
• Sending OS‑level notifications through Electron.

Stay upright, stay productive! 🚀

Using MediaPipe

• Applying Geometry Logic to detect poor posture.
• Triggering Electron Notifications to alert the user.

Next Steps

• Add a “calibration” button so users can set their “perfect” posture as a baseline.
• Add a dashboard to track “Slouch Frequency” over time.

What do you think? Would you trust an AI to judge your posture, or is this a bit too “Big Brother”? Let me know in the comments! 👇