Nutrition Lens

• 

  Detect the Coke

Inspiration

1. The fitness industry has attracted a lot of consumers.
2. Fitness watches, bands, and trackers make consumers easy to track their activity and burnt calories/energy.
3. But energy intake is hard to track. Currently, users enter data manually.
   • time-consuming
   • hard to estimate
   • no useful functions
   • ultimately discourage from using

What it does

• Allowing people to get a quick access to know the foods' nutrition.
• Providing a different view of displaying information as usual.

Target users

• Individuals who willing to spend little time for a brief nutrition overview.
• Athletes, professional trainers who track their nutrition intake seriously.

How I built it

Technologies

• Big Data: gather product images, nutrition facts
• Machine Learning: calculate cascade classifiers, recognize objects
• Augmented Reality (AR): display / show nutrition facts
• Object Tracking
• React Native: deploy web app to both iOS and Android platforms

Process

We are really collaborative!

• Wangyue Wang: Front-end design and implementation, packing web app to different platforms
• Xiaofan Ni: Object tracking and AR part
• Tianyu Wang: Building objects' cascade classifiers and gathering nutrition information

Open Source Projects We Used

• tracking.js
• react-native
• OpenCV

Challenges I ran into

Training the object model is some sort of difficult in the 48 hours.

Accomplishments that I'm proud of

But we've done!

What's next for Nutrition Lens

• Nutrition overview
• Item size detection for better accuracy.
• Reorder like Amazon Dash (re-order)
• Advertisers (recommend competitive products)
• Sponsors (recommend sponsored products)
• Connect w/ apps (API)
• Reminder

Built With

• ar
• big-data
• computer-vision
• machine-learning
• object-tracking
• opencv
• react-native