Ducks, Sensors & Agents: Hands-On Edge AI with Arduino UNO Q

By the end of this workshop, attendees will be able to: Part 1 – Computer Vision: 1. Describe the end-to-end Edge AI workflow, using agentic AI coding throughout: (a) Collect data and label images with bounding boxes; (b) Design a machine learning model with Edge Impulse; (c) Train & test the model; (d) Create a reusable Edge Impulse skill for the AI agent, capturing the workflow conventions (API usage, project structure, deployment steps) so the agent can reliably assist on future Edge AI projects. 2. Re-train an Object Detection model (FOMO / MobileNetV2-SSD) on a custom "rubber ducks" dataset: (a) Test the new model on your mobile phone, using the camera and the browser; (b) Deploy on Arduino UNO Q with the AppLab integration, using the AI agent (with the Edge Impulse skill) to generate and iterate on the application code — camera capture, inference loop, and output handling; (c) Run and test the model on the Arduino UNO Q, prompting the agent to debug errors and add features (e.g., counting ducks, triggering an output on detection, etc.). Part 2 – MCU Accelerometer Triggering MPU: 1. Collect accelerometer data & train the model 2. Deploy to the ST MCU on Arduino Uno Q 3. Use a Bridge library to send data from MCU to MPU