FitElderAI

FitElderAI: Your AI Fitness Coach for Upper Body Strength

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Introduction

As seniors age, maintaining upper body strength through exercises like arm curls and sit-ups becomes crucial. Traditional methods for ensuring correct exercise form and tracking performance are often inaccessible or inadequate for older adults.

Welcome to FitElderAI, an advanced web application designed to assess upper body fitness and flexibility for senior citizens. Leveraging AI and computer vision technologies, FitElderAI offers a comprehensive and research-oriented approach to evaluating fitness through two well-established senior fitness tests: the Arm Curl Test and the Chair Stand Test.

Research Proposal:

“Using Computer Vision for Real-Time Exercise Form Analysis and Feedback for Seniors”

Solution:

I developed FitElderAI, a web app that uses computer vision and AI to count arm curls and sit-ups, providing real-time data collection. This tool aims to improve senior fitness by offering accessible, user-friendly, and effective exercise monitoring solutions.

How It Aligns:

FitElderAI demonstrates practical applications of computer vision and AI in fitness for seniors, offering a base for research on exercise analysis, feedback mechanisms, and their effects on senior health and fitness.

What It Does

FitElderAI offers:

  • Upper Body Flexibility Testing: Evaluates fitness using the Arm Curl Test and Chair Stand Test.

How I Built It

I built FitElderAI using the following technologies and methods:

  • Programming Languages: Python
  • Frameworks: Streamlit for web app development
  • Libraries: OpenCV and MediaPipe for computer vision; Matplotlib for data visualization
  • APIs: No external APIs used, but potential for future integrations
  • Cloud Services: Deployed on Streamlit Community Cloud

Challenges I Ran Into

Some key challenges included:

  • Dependency Management: Ensuring all required libraries were available in the deployment environment.
    • Data Accuracy: Fine-tuning the computer vision models to accurately detect test performance.
    • User Interface Design: Creating an intuitive interface that accommodates various user needs. I overcame these challenges through thorough testing, iterative design, and leveraging community forums for support.

Accomplishments That I’m Proud Of I’m proud of:

  • Successful AI Integration: Developing accurate AI models for fitness testing.
  • Research Focus: Designing features to support future research into senior fitness programs.

What I Learned

Through this project, I learned:

  • Advanced AI Techniques: Improved my skills in computer vision and AI model integration.
  • Web Development Practices: Gained experience in building and deploying web applications.
  • Research Methodologies: Explored how to structure a project for research purposes.

What’s Next for FitElderAI

The future of FitElderAI includes:

  • Expanding Test Variations: Introducing more fitness tests and conditions.
  • Enhancing Collaboration Tools: Adding features for collaborative research and data sharing.
  • Long-Term Research Studies: Using collected data to conduct longitudinal studies on senior fitness.

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