This is a submission for the Google AI Studio Multimodal Challenge
What I Built
I built an AI Fitness Coach that analyzes exercise form from uploaded videos. This application helps users improve their squat and deadlift techniques by providing real-time feedback, identifying form issues, and suggesting corrective exercises.
Learning proper exercise form can be intimidating for beginners. Asking for help at the gym can feel uncomfortable, YouTube tutorials can be overwhelming with contradictory information, and personal trainers are often prohibitively expensive. The AI Fitness Coach aims to lower these barriers by providing an accessible, judgment-free way to check your form and get personalized feedback.
The AI Fitness Coach solves several problems:
- Provides accessible form checks without requiring a human trainer
- Helps prevent injuries by identifying risky movement patterns
- Delivers personalized exercise recommendations based on specific form issues
- Offers an educational tool for fitness enthusiasts to improve technique
- Reduces the intimidation factor for beginners wanting to learn proper form
The GIF above demonstrates the key functionality of the AI Fitness Coach:
- Uploading an exercise video
- AI analysis of the movement
- Receiving detailed form feedback
- Reviewing specific issues with timestamps
How I Used Google AI Studio
I leveraged Google AI Studio's Gemini Pro Vision model to power the core functionality of the AI Fitness Coach. The implementation involved:
Video Analysis: Using Gemini Pro Vision to analyze exercise videos and evaluate form
Structured Response Generation: Configuring the model to return consistent JSON-formatted feedback
Prompt Engineering: Developing precise prompts that instruct the model to identify specific form issues in squat and deadlift exercises
Visual Understanding: Utilizing the model's ability to perceive movement patterns and body positioning across video frames
Multimodal Features
Form Analysis: Gemini identifies specific form issues like improper back angle, knee positioning, or weight distribution based on visual cues in the video.
Structured Feedback: The application parses the model's analysis into structured data that includes:
- Overall form score
- Positive aspects of the user's technique
- Specific form issues with timestamps
- Recommended exercises to address identified problems
- Sources for recommendations
Technical Implementation
The application is built with:
- Frontend: HTML, CSS, JavaScript
- Backend: FastAPI (Python)
- AI Integration: Google AI Studio's Gemini Pro Vision model
- Deployment: Hosted on Vercel
The system processes video uploads, sends them to the Gemini API for analysis, and presents the results in an intuitive interface that allows users to review specific issues with synchronized video playback.
Important Disclaimer
This application is a proof of concept for demonstration purposes only. While the AI provides valuable feedback, it should be used with caution:
- The AI may occasionally misinterpret movements or provide incorrect advice
- Users should always prioritize their safety and comfort when exercising
The AI Fitness Coach demonstrates the potential of multimodal AI to make fitness guidance more accessible, but it's important to recognize its limitations and use it responsibly.
Contributors
This is a solo dev project by myself.
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