In the last 24 hours, Americans have spent another $62.74 million dollars on skincare. Yet for the consumer, finding the best product remains a challenge: skincare is personal, shaped by genetics and preferences, with ever-changing needs influenced by environmental factors and lifestyle habits. For many, it’s more of an art than a science, involving trial and error that leads to wasted time, money, and, in the worst cases, breakouts and rashes. Adding to the confusion is the overwhelming marketing noise. While ingredient lists are transparent, differences in concentrations, application methods, and combinations with other active ingredients lead to vastly different outcomes. The only way to cut through this complexity is to track and analyze one’s own use history, identifying the patterns that emerge from correlations of usage and results. Alongside our treat-and-track skincare wearable hardware, this project aims to develop a user-friendly, mobile skincare management tool by employing computer vision (for product recognition), cloud computing (for backend storage and analysis), machine learning (for pattern identification and personalized recommendations), and web scraping (to aggregate product ingredient data). With this tool, users can confidently find effective and safe solutions tailored to their unique skin needs.

Semester/Phase 1 (Foundational Work) Primary priority: build a functional backend and core product recognition system 1. Backend Development: set up a scalable database structure for storing product data and user logs. Develop APIs for user login, product storage, and information retrieval. 2. Product Recognition System: develop a functional text recognition prototype by using ready-built libraries, such as Google Cloud Vision API, on an iOS mobile app. 3. Data Aggregation: build a simple web scraping tool or use available libraries to collect product ingredient information and store it in the database. Semester/Phase 2 (Feature Expansion) Primary priority: enhance functionality and usability 1. Real-time Product Recognition: enable direct identification of the product through the iOS app’s camera interface. 2. Logging System: build the backend and iOS app interface for users to log and track products used manually or via product recognition. Include functionality for setting recurring use reminders. 3. Forum Feature: create a basic forum where users share product experiences. Implement backend support for storing posts and comments.