{"id":36853,"date":"2026-10-28T15:53:18","date_gmt":"2026-10-28T14:53:18","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=36853"},"modified":"2026-10-28T15:53:20","modified_gmt":"2026-10-28T14:53:20","slug":"arduino-brings-edge-ai-to-the-mirror-real-time-style-advice-with-the-arduino-ventuno-q-board","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/iot\/arduino-brings-edge-ai-to-the-mirror-real-time-style-advice-with-the-arduino-ventuno-q-board\/","title":{"rendered":"Arduino Brings Edge AI to the Mirror: Real-Time Style Advice with the Arduino\u00ae VENTUNO\u2122 Q board"},"content":{"rendered":"\n

An open source project combines computer vision, multimodal models, and local artificial intelligence to turn an ordinary mirror into an interactive fashion assistant, with no dependence on the cloud.<\/em><\/p>\n\n\n\n

Mirrors have long been depicted as magical objects across popular fiction. They serve as portals to alternate worlds in Alice in Wonderland and The Matrix, clairvoyant windows in Snow White and Beauty and the Beast, a dating game showcase in Shrek, and a window into one’s deepest yearnings with the Mirror of Erised in Harry Potter.<\/p>\n\n\n\n

To this gallery of magical artifacts, we wanted to add our own contribution, with a pinch (so to speak) of technology.<\/p>\n\n\n\n

Imagine standing in front of a mirror that doesn\u2019t just reflect your image, but can also analyze what you\u2019re wearing and suggest how to improve your outfit. That\u2019s the idea behind AI Smart Mirror<\/strong>, a project that explores the potential of Edge AI through the VENTUNO<\/strong> Q<\/strong> platform.<\/p>\n\n\n\n

The solution combines visual recognition, AI models, and an interactive interface to deliver an experience that could be used in smart fitting rooms, next-generation retail, and connected home environments.<\/p>\n\n\n\n

The project is available on GitHub and is based on the original Arduino Smart Mirror tutorial, with one significant change to the interaction: instead of a physical button, it uses a visual activation system that starts the analysis naturally and without contact.<\/p>\n\n\n\n

Two AI Models to Understand What We See<\/strong><\/h3>\n\n\n\n

The heart of the system is the collaboration between two AI components, each with a specific job.<\/p>\n\n\n\n

The first is a custom vision model, built with Edge Impulse, an end-to-end platform for building and deploying machine learning models on edge devices, that analyzes the camera\u2019s video stream. When it recognizes the visual class chosen as the trigger, it activates the mirror\u2019s processing. This way, the application can wait for a specific visual signal before starting the analysis, without requiring any user intervention.<\/p>\n\n\n\n

The second component is Qwen3-VL-4B-Instruct, a Vision-Language Model (VLM). Unlike a system that merely classifies images, a VLM can interpret visual content and produce natural language responses. In the project, the model receives a camera frame along with a prompt and generates the feedback shown next to the reflected image.<\/p>\n\n\n\n

Combining the two models separates the initial detection from the more elaborate interpretation: first the system recognizes the signal that should activate it, then it analyzes the image and returns a response.<\/p>\n\n\n\n

VENTUNO<\/strong> Q<\/strong>: Advanced Artificial Intelligence Directly on the Device<\/strong><\/h3>\n\n\n\n

This architecture runs on VENTUNO<\/strong> Q<\/strong>, a platform designed for Physical AI, computer vision, and advanced robotics applications.<\/p>\n\n\n\n

The board uses a dual-brain architecture. The Qualcomm Dragonwing\u2122<\/sup> IQ8 processor handles the most demanding workloads, including AI model inference, while the STM32H5 microcontroller is dedicated to deterministic control and tasks that require predictable response times.<\/p>\n\n\n\n

This combination brings perception and control functions together on a single platform. Development happens through Arduino\u00ae<\/sup> App Lab, the environment that unites tools for programming, AI model integration, and application building.<\/p>\n\n\n\n

In the Smart Mirror\u2019s case, the software is built by combining App Lab Bricks, modular building blocks that handle image classification, frame capture, processing through the multimodal model, and displaying the results in a web interface.<\/p>\n\n\n\n

Why Edge AI Makes the Difference<\/strong><\/h3>\n\n\n\n

The most interesting aspect of the project isn\u2019t just its use in the fashion sector, but how artificial intelligence is run.<\/p>\n\n\n\n

In many applications, images are sent to a remote service that handles the processing and returns a response. With Edge AI, instead, inference runs directly on local hardware.<\/p>\n\n\n\n

This approach offers several advantages:<\/p>\n\n\n\n