Say Goodbye to "Tech Neck": Building a Real-Time AI Posture Coach with MediaPipe and Electron 💻🧘‍♂️

We've all been there. You start your workday sitting upright like a disciplined monk, but four hours into a debugging session, you’re hunched over your keyboard looking like a question mark. "Tech neck" and spinal misalignment aren't just uncomfortable—they are long-term health risks for developers.

In this tutorial, we are going to build a desktop application that uses real-time pose estimation and computer vision for health to monitor your posture. By leveraging MediaPipe skeleton detection and the cross-platform power of Electron, we'll create a system that calculates anatomical angles and whispers (or shouts) at you when you start slouching. This is "Learning in Public" at its finest—turning a personal problem into an engineering solution!

The Architecture 🏗️

The system works by capturing a video stream from your webcam, processing each frame through a machine learning model to identify key body landmarks, and then applying trigonometric calculations to determine if your posture is within "healthy" thresholds.

graph TD
    A[Webcam Feed] --> B[Electron Renderer Process]
    B --> C[MediaPipe Pose Engine]
    C --> D{Landmark Detection}
    D -->|Coordinate Data| E[Anatomical Angle Calculator]
    E --> F{Threshold Check}
    F -->|Bad Posture| G[Voice/OS Notification]
    F -->|Good Posture| H[Keep Monitoring]
    G --> B

Prerequisites 🛠️

Before we dive into the code, ensure you have the following in your toolkit:

• MediaPipe: Google’s high-performance ML framework for customizable ML solutions.
• OpenCV (via JavaScript): For frame manipulation if needed.
• Electron: To package our solution as a desktop app that runs in the background.
• TypeScript: Because type safety makes our skeleton detection much more robust.

Step 1: Setting up the Electron Environment

We want our app to live in the system tray and monitor us while we work. First, initialize your project and install the necessary dependencies:

npm init -y
npm install electron @mediapipe/pose @tensorflow/tfjs-core @tensorflow/tfjs-converter
npm install --save-dev typescript

Step 2: Pose Detection Logic 🦴

The heart of our application lies in identifying the relationship between the ear, the shoulder, and the hip. If the angle between these three points deviates significantly from a vertical line, we know the user is slouching.

Here is the core TypeScript logic for calculating the "Slouch Angle":

// types/pose.ts
export interface Landmark {
  x: number;
  y: number;
  z: number;
}

// Logic to calculate angle between three points
export const calculateAngle = (a: Landmark, b: Landmark, c: Landmark): number => {
  const radians = Math.atan2(c.y - b.y, c.x - b.x) - Math.atan2(a.y - b.y, a.x - b.x);
  let angle = Math.abs((radians * 180.0) / Math.PI);

  if (angle > 180.0) {
    angle = 360 - angle;
  }
  return angle;
};

Step 3: Implementing the MediaPipe Pipeline

We will use the @mediapipe/pose solution to get 33 3D landmarks in real-time. We specifically care about landmarks: EAR_LEFT, SHOULDER_LEFT, and HIP_LEFT (or the right side equivalents).

import { Pose, POSE_LANDMARKS } 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((results) => {
  if (!results.poseLandmarks) return;

  const leftEar = results.poseLandmarks[POSE_LANDMARKS.LEFT_EAR];
  const leftShoulder = results.poseLandmarks[POSE_LANDMARKS.LEFT_SHOULDER];
  const leftHip = results.poseLandmarks[POSE_LANDMARKS.LEFT_HIP];

  const trunkAngle = calculateAngle(leftEar, leftShoulder, leftHip);

  // If the angle is too sharp, the head is too far forward
  if (trunkAngle < 150) {
    triggerAlert("Hey! Sit up straight! 🦴");
  }
});

Advanced Patterns & Production Readiness 🚀

While this prototype works great for a weekend project, building a production-grade AI health assistant requires handling edge cases like lighting variations, varying camera angles, and minimizing CPU usage so it doesn't lag your IDE.

If you are looking for more production-ready examples, advanced architectural patterns for AI locally-hosted apps, or deep dives into optimizing MediaPipe for low-end hardware, I highly recommend checking out the WellAlly Tech Blog. It's a goldmine for developers looking to bridge the gap between "it works on my machine" and "it scales for thousands of users."

Step 4: Adding the "Voice of Reason" 🔊

To make the app truly effective, we can use the Web Speech API to give the user a gentle nudge without them needing to look at a notification popup.

function triggerAlert(message: string) {
  const speech = new SpeechSynthesisUtterance(message);
  speech.rate = 1.2;
  window.speechSynthesis.speak(speech);

  // Also send an OS-level notification
  new Notification("Posture Check", { body: message });
}

Conclusion: Health is the Ultimate Developer Productivity Hack 🥑

We spend most of our lives in front of these glowing rectangles. Using a bit of TypeScript and Computer Vision to look after our physical well-being isn't just a fun project—it's an investment in your career longevity.

Summary of what we built:

1. Real-time capture using Electron and a webcam.
2. Landmark detection via MediaPipe to identify spinal alignment points.
3. Trigonometric analysis to detect slouching.
4. Audio-visual feedback to correct behavior.

What features would you add? Eye-strain detection? Water break reminders? Let me know in the comments! And don't forget to visit wellally.tech/blog for more advanced AI engineering content.

Stay hydrated and sit up straight! 🚀✨