Day 6: Personalized AI Workout Recommendations #AIWorkout #FitnessAI

AI-driven workout recommendations enhance training efficiency by tailoring exercises based on user performance, fitness level, and real-time data. We’ll use machine learning models to suggest customized workouts and dynamically adjust routines.

Table of Contents

1. Collect User Workout Data

Tracking workout performance is essential. Store data like:

Repetitions & Sets: Number of times an exercise is performed.
Form Accuracy: Captured via pose detection (from Day 5).
Heart Rate: Use Apple Health or Google Fit APIs (Google Fit API)
Workout Duration: Time spent on each exercise.

For React Native, fetch heart rate data using Google Fit API:

import GoogleFit, { Scopes } from 'react-native-google-fit';

const options = {
  scopes: [Scopes.FITNESS_ACTIVITY_READ, Scopes.FITNESS_BODY_READ],
};

GoogleFit.authorize(options)
  .then(() => {
    GoogleFit.getHeartRateSamples({ startDate: '2024-01-01', endDate: new Date().toISOString() })
      .then((res) => console.log(res))
      .catch((err) => console.log(err));
  })
  .catch((err) => console.log(err));

2. Train a Machine Learning Model for Workout Suggestions

Use TensorFlow.js to predict recommended exercises based on past performance.

1. Install TensorFlow.js

npm install @tensorflow/tfjs

2. Train the AI Model

import * as tf from '@tensorflow/tfjs';

// Sample workout dataset (reps, duration, accuracy) => next recommended workout type
const trainingData = tf.tensor2d([
  [20, 30, 90], // Squats, 30 seconds, 90% accuracy
  [15, 25, 80], // Push-ups, 25 seconds, 80% accuracy
  [10, 20, 70], // Lunges, 20 seconds, 70% accuracy
]);

const workoutLabels = tf.tensor2d([
  [1, 0, 0], // Suggest Squats
  [0, 1, 0], // Suggest Push-ups
  [0, 0, 1], // Suggest Lunges
]);

const model = tf.sequential();
model.add(tf.layers.dense({ inputShape: [3], units: 10, activation: 'relu' }));
model.add(tf.layers.dense({ units: 3, activation: 'softmax' }));

model.compile({ optimizer: 'adam', loss: 'categoricalCrossentropy' });

async function trainModel() {
  await model.fit(trainingData, workoutLabels, { epochs: 100 });
  console.log('Training complete');
}

trainModel();

3. Get AI-Based Workout Recommendations

Once trained, use real-time data to suggest exercises dynamically.

async function getWorkoutSuggestion(reps, duration, accuracy) {
  const inputTensor = tf.tensor2d([[reps, duration, accuracy]]);
  const prediction = model.predict(inputTensor);
  const result = await prediction.data();
  
  const workoutTypes = ['Squats', 'Push-ups', 'Lunges'];
  return workoutTypes[result.indexOf(Math.max(...result))];
}

getWorkoutSuggestion(18, 28, 85).then(console.log);

4. Provide AI-Generated Voice Feedback

Use Expo Speech API to announce the next workout.

import * as Speech from 'expo-speech';

getWorkoutSuggestion(18, 28, 85).then((exercise) => {
  Speech.speak(`Next, do ${exercise} for 30 seconds.`);
});

This AI-driven recommendation system analyzes user progress and dynamically adjusts workouts, mimicking a real personal trainer.

Next: Day 7 – AI-Powered Real-Time Workout Tracking #AIWorkout #LiveTracking

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