The CES Award-Winning CareOS Artemis Smart Mirror Highlights Privacy Benefits and User Experience Enhancements Made Possible by TensorFlow’s Edge AI
CareOS, a digital platform for personal care, is presenting the CES award-winning CareOS Artemis Smart Mirror at Google I/O, demonstrating the practical user experience and privacy benefits of using Google’s TensorFlow Lite in an edge AI application.
CareOS is the first platform for personal care that combines the privacy, user experience and functionality needed to create a satisfying digital environment in the bathroom, salon, spa and other places we engage in self care. CareOS was first conceived in the Baracoda Lab by experts in connected devices, wellness and digital design. The platform securely combines and enhances data from smart personal care devices and digital services, improving the user’s health and appearance. The touchless smart mirror interface provides access to all of the data and insight users need to live longer and better lives.
“TensorFlow Lite’s edge AI gives us a wealth of power to provide useful and engaging information to improve people’s preventative care, beauty routines and hygiene rituals, while keeping all data local,” said Chloé Szulzinger, co-founder & head of marketing-communications of CareOS.
CareOS is working with leading beauty and wellness brands on custom platforms for use in salons, spas and retail. The demonstration provided at Google I/O will replicate this experience with visitors standing in front of the CareOS Artemis mirror where they can virtually change their lipstick and hair styles or try on new glasses.
“Edge AI is often considered valuable for its speed, but CareOS shows that the localized performance of TensorFlow Lite unlocks possibilities that go far beyond reducing lag,” said Tim Davis, Product Manager for Google AI. “CareOS is in a position to change health outcomes by arming people with instant feedback on their practices and suggesting preventative care right where they are when these questions arise. It’s a shining example of the very practical ways TensorFlow Lite make new models possible.”