Day 5: AI-Driven Workout Analysis & Form Correction #AIWorkout #FormCorrection #PoseDetection

Table of Contents

Overview

Today, we’ll implement AI-driven workout analysis and form correction using real-time pose detection with TensorFlow.js or MediaPipe. The goal is to analyze the user’s movements, detect improper form, and provide voice-guided feedback to correct posture during workouts.

Step 1: Integrate Pose Detection Library

We will use TensorFlow.js with MoveNet or PoseNet for real-time body tracking.

1. Install Dependencies

If you haven’t installed TensorFlow.js yet, do so now:

npm install @tensorflow/tfjs @tensorflow-models/pose-detection @mediapipe/pose

2. Load the Pose Detection Model

Create a poseDetection.js file and load the MoveNet Lightning model:

import * as posedetection from "@tensorflow-models/pose-detection";
import * as tf from "@tensorflow/tfjs";

let detector;

export const loadPoseDetector = async () => {
  detector = await posedetection.createDetector(
    posedetection.SupportedModels.MoveNet,
    { modelType: "Lightning" }
  );
};

export const detectPose = async (videoElement) => {
  if (!detector) return null;
  return await detector.estimatePoses(videoElement, { flipHorizontal: false });
};

Step 2: Capture Live Camera Feed

Modify your React Native component to display a live camera feed and pass it to the pose detector.

1. Install Expo Camera

expo install expo-camera

2. Create the Camera Component

import { Camera } from 'expo-camera';
import { useRef, useEffect, useState } from 'react';
import { View, Button, Text } from 'react-native';
import { loadPoseDetector, detectPose } from './poseDetection';

export default function WorkoutAnalyzer() {
  const cameraRef = useRef(null);
  const [hasPermission, setHasPermission] = useState(null);
  const [poseFeedback, setPoseFeedback] = useState("");

  useEffect(() => {
    (async () => {
      const { status } = await Camera.requestPermissionsAsync();
      setHasPermission(status === 'granted');
      await loadPoseDetector();
    })();
  }, []);

  const analyzePose = async () => {
    if (!cameraRef.current) return;
    const poseData = await detectPose(cameraRef.current);
    
    if (poseData && poseData[0]) {
      const leftKnee = poseData[0].keypoints.find(p => p.name === 'left_knee');
      const rightKnee = poseData[0].keypoints.find(p => p.name === 'right_knee');

      if (leftKnee.y < rightKnee.y + 10) {
        setPoseFeedback("Good form! Keep going.");
      } else {
        setPoseFeedback("Correct your knee alignment!");
      }
    }
  };

  return (
    <View>
      <Camera ref={cameraRef} style={{ width: '100%', height: 400 }} />
      <Button title="Analyze Pose" onPress={analyzePose} />
      <Text>{poseFeedback}</Text>
    </View>
  );
}

Step 3: Voice Feedback for Form Correction

Since we already have voice output from Day 4, let’s trigger voice feedback when a posture issue is detected.

1. Install Expo Speech API

expo install expo-speech

2. Add Speech Feedback

Modify the analyzePose function:

import * as Speech from 'expo-speech';

const analyzePose = async () => {
  if (!cameraRef.current) return;
  const poseData = await detectPose(cameraRef.current);

  if (poseData && poseData[0]) {
    const leftKnee = poseData[0].keypoints.find(p => p.name === 'left_knee');
    const rightKnee = poseData[0].keypoints.find(p => p.name === 'right_knee');

    if (leftKnee.y < rightKnee.y + 10) {
      setPoseFeedback("Good form! Keep going.");
      Speech.speak("Good form! Keep going.");
    } else {
      setPoseFeedback("Correct your knee alignment!");
      Speech.speak("Correct your knee alignment.");
    }
  }
};

Final Result 🎯

The camera captures the user’s workout in real-time.
The pose detector analyzes form and posture.
If form is incorrect, voice feedback guides the user.

What’s Next? 🔥

➡️ Day 6: Personalized Workout Recommendations with AI
We’ll implement customized workout plans based on user performance, fitness goals, and AI recommendations.

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