The problem BeFit. solves

BeFit addresses several key challenges in the realm of fitness and motivation:

1. Lack of Motivation: Many people struggle to find the motivation to exercise regularly. BeFit introduces a novel approach by blending the spontaneity of social media challenges with the accountability of fitness tracking apps. This combination keeps motivation high by introducing unpredictability and fun into the routine.

2. Social Support: Research shows that social support can significantly enhance exercise adherence. BeFit leverages this by allowing users to view friends' accomplishments and cheer them on, creating a supportive community. This social aspect turns solitary exercise routines into a shared journey.

3. Verifiable Progress: Traditional fitness apps rely on self-reporting, which can be inaccurate or misleading. BeFit uses machine learning to verify exercise completion through the camera, providing an objective measure of activity. This ensures that rewards (in the form of NFTs) are earned genuinely, making progress tangible and meaningful.

4. Rewards for Consistency: Maintaining a consistent exercise routine is challenging. BeFit introduces gamification elements similar to language learning apps, rewarding users with unique NFTs for regular activity. This not only serves as a record of achievements but also adds a collectible and competitive element to exercise.

5. Financial Incentives: Beyond virtual rewards, BeFit incorporates financial incentives by allowing users to send and receive USDC tokens as rewards for meeting fitness goals. This direct financial motivation can significantly enhance the appeal of sticking to fitness routines.

6. Engagement through Competition: The Live Showdown feature introduces a competitive edge, allowing users to participate in real-time challenges. This not only increases engagement but also adds an entertaining aspect to exercising, making it more appealing to a broader audience.

Challenges we ran into

Converting our Python machine learning code for pushup recognition into JavaScript for browser and mobile compatibility was a significant hurdle in developing BeFit. This transition was necessary to integrate our model directly into the web experience, ensuring universal access without backend processing. Here's how we tackled it:

1. Adoption of TensorFlow.js: We explored TensorFlow.js to run our machine learning models in the browser, necessitating the optimization and conversion of our Python model for web suitability.

2. Model Optimization: The model was pruned and quantized to reduce size and complexity, ensuring smooth browser performance.

3. Rewriting Application Logic: We rewrote the core application logic in JavaScript, adapting to the web environment's asynchronous nature and managing video streams for real-time detection.

4. Extensive Testing: The application underwent rigorous testing across various devices and browsers, leading to iterative adjustments for compatibility and performance.

5. Community Engagement: Leveraging the TensorFlow.js community resources was crucial for overcoming technical challenges.

6. Fallback Strategies: For incompatible devices or browsers, we implemented simplified algorithms and recommended more suitable platforms for users.

This process enabled BeFit to offer a real-time, interactive fitness experience directly within users' browsers, significantly enhancing the app's accessibility and user engagement. It highlighted the importance of adaptability and innovation in solving complex technical challenges.