Custom Workout App: Powered by Image Analysis AI

This fitness app leverages Vision-Language Models (VLMs) and advanced image processing to analyze full-body composition and track muscle development. With personalized insights and adaptive workout recommendations, users gain a tailored experience that evolves with their fitness journey.

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OVERVIEW

Project Overview

We built a fitness app that uses cutting-edge AI to assess body composition and provide users with precise, personalized workout plans. By combining computer vision with smart recommendation engines, the app offers a transformative experience tailored to individual goals.

Our team tested multiple AI models and APIs to ensure reliable measurement and feedback. Solutions like OpenAI GPT-4 Vision, Gemini 1.5 Pro, and LLaMA 3.2 Vision were evaluated for their effectiveness in analyzing body features.

After rigorous testing, we selected the most reliable solution, incorporating the 3D Look API for its superior performance in body measurement.

Key Features

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01.

Body Composition Analysis

  • Users upload a full-body image and input basic details (height, weight, age).
  • The AI evaluates proportions, dimensions, and body symmetry to offer a comprehensive analysis.
  • The system provides score-based insights across various body zones (e.g., upper body, core, lower body), allowing users to track improvements in targeted areas.

02.

Personalized Workout Plans

  • The app generates daily workout routines based on users’ specific needs.
  • It identifies underdeveloped areas and suggests targeted exercises, while also maintaining balance by reinforcing stronger zones.
  • These adaptive plans evolve based on progress, ensuring workouts remain effective and engaging.
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03.

Workout Streaks

  • The app keeps users motivated by tracking workout consistency and performance trends.
  • Visual feedback, score comparisons, and goal-setting features help users stay accountable and focused.

Features at a Glance

  • trumanAI-driven full-body analysis

    trumanVision-language model integration

    truman3D Look API for precise measurements

    trumanCustom GPT integration for recommendations

    trumanAdaptive workout planning

    trumanReal-time progress visualization

    trumanSecure login with OAuth 2.0

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Project Challenges and Their Solutions

1. Inconsistent AI Model Performance

Challenges

During the Proof of Concept (PoC), we tested several AI models (OpenAI GPT-4 Vision, Gemini 1.5 Pro, and LLaMA 3.2 Vision), but the results were inconsistent.

Some models produced varying outcomes even when analyzing the same image, leading to accuracy concerns.

SOLUTIONS

To address this, we used a third-party API for body measurements and developed a custom AI model and fine-tuned it for better performance, ensuring more consistent and reliable results. This approach helped minimize the variability in analysis and improved the accuracy of the body scoring system.

2. Scalability and Cost Management

Challenges

The use of AI models such as OpenAI’s GPT-4 resulted in higher operational costs, especially with large datasets and multiple API calls for image processing.

SOLUTIONS

We optimized the use of tokens and API calls to minimize costs while maintaining accuracy. By refining the workflow and managing API usage efficiently, we ensured that the app could scale without excessive costs.

Tech Stack

React Native

A popular framework for building cross-platform mobile applications. React Native allowed us to develop the app for both iOS and Android with a single codebase.

PostgreSQL

PostgreSQL was used to store user data, workout logs, and other critical information, ensuring efficient querying and data integrity.

                                                                                                                                                                                                                                                                                                                 

3D Look API

This API provided the necessary body measurements and was integrated for accurate hip, waist, and thigh measurements.

OAuth 2.0

Used for secure user authentication, allowing users to sign up and log in with their preferred credentials (Google, Facebook, etc.) while maintaining data security.

AWS (Amazon Web Services)

Used for cloud hosting and scalability, ensuring high performance during traffic spikes.

Want to Build an AI Fitness App?

If you have an AI-powered fitness app idea in mind and want to execute it right away. Feel free to reach out to Biz4Group's AI experts.

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