Code Integration & Performance Tune-Up for a Skin-Health iOS App

Morphace builds wearable devices that track skin hydration and rejuvenation signals. Our device streams numeric data (via Bluetooth) to an iOS app, which stores everything in an AWS RDS (PostgreSQL) database through a small REST API. Working alongside our internal team of 2 software engineers over the summer, your focus is to integrate our existing code and optimize existing features for response time. As the core feature is data visualization and trend analysis, there is potential to implement existing ML models for numerical trend detection.

Adaptive Wearable Technology Enhancement for Skincare

Morphace Inc. is focused on advancing its wearable technology, which promotes skin cell remodeling through non-invasive electrodes on facial skin. The current project aims to enhance the device by introducing adjustable output settings, allowing users to customize the intensity of the stimulation according to their needs. Additionally, the project will involve implementing a sequential activation mechanism for the electrodes, optimizing the therapeutic process by targeting specific areas in a controlled manner. Another key aspect of the project is transitioning the device to a portable power source, increasing its usability and convenience for users on the go. This project provides an opportunity for learners to apply their knowledge in electronics, programming, and biomedical engineering to create a more versatile and user-friendly product. - Enable adjustable device output for personalized user experience. - Implement sequential activation of electrodes for targeted therapy. - Transition to a portable power source for enhanced mobility.

Product Design & Prototype

Morphace is a startup that produces data driven mask devices that treats wrinkles and tracks skin condition on a paired app. Morphace aims to refine the aesthetic design of our skincare mask devices to better align with customer preferences and branding. This project will focus on enhancing the mask's visual appeal and ergonomics to increase market competitiveness and customer satisfaction.

A Mobile Smart Lens and Field Guide for Skincare

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.