HealthTracker

Inspiration

Suicide is the second leading cause of death among young people aged 10-24. In total, about 65.1 million girls and 94.2 million boys aged 5-19 were living with obesity globally in 2022. A systematic review found that about 22% of children and adolescents showed signs of disordered eating behaviors, with a higher prevalence among girls and older adolescents. These disturbing facts inspired us to create an app that addresses these issues. To help user’s mental health, we provide them with daily affirmations and mental health resources. To promote healthy eating choices, we help users determine if their meals are nutritious and allow them to check if the food items they’re eating help them achieve their goals. Furthermore, to help those struggling with their weight feel more comfortable in their bodies, we analyze users' exercise forms and give them tips on how to be more efficient and perform the exercises correctly.

What it does

HealthTracker is an Xcode app that combines physical and mental health. It collects user info, provides daily affirmations, links to mental health resources, analyzes meals, scans food labels, and generates fitness plans with real-time feedback.

How we built it

We built HealthTracker using Xcode with SwiftUI and integrated three components: A Machine Learning-powered meal image classification model using a Kaggle dataset. Apple's Vision Framework for text extraction from food labels. A fitness analyzer using AV Foundation and Apple Vision for exercise guidance.

Challenges we ran into

Training the ML model to classify diverse food types accurately. Extracting and analyzing text data from complex food labels. Fine-tuning real-time exercise detection for accurate feedback.

Accomplishments that we're proud of

Seamlessly integrating ML and Apple frameworks to create a holistic health app. Building an app that supports both physical and mental health goals. Designing an easy-to-use interface with meaningful affirmations and mental health resources.

What we learned

How to train ML models for food classification and integrate them into an app. Using Apple's Vision Framework for text recognition. Using AV Foundation and text extraction technology

What's next for HealthTracker

We plan to release Health Tracker on app store so that it is accessible to everyone.

Built With

• apple-vision-framework
• avfoundation
• createml
• swift
• xcode