11:00–11:30 AM — Challenge Analysis & Brainstorming

Reviewed the on-device gesture-recognition challenge and defined the three required actions: Pause, Confirm, and Skip. Discussed the overall system architecture and implementation approach.

11:30 AM–12:00 PM — Team Task Division
Divided responsibilities based on team members’ strengths, including data collection and model development, software deployment, hardware integration, and research/system design.

12:00–1:00 PM — Hardware Setup
Examined the Arduino UNO Q, Modulino Pixels, Modulino Buzzer, camera, and Qwiic connections. Built the initial hardware chain and verified the physical connections.

1:00–2:00 PM — Research & Solution Design
Researched relevant gesture-recognition approaches and datasets. Evaluated lightweight model options suitable for on-device inference and discussed model-size and latency constraints.

2:00–3:00 PM — Data Strategy & Collection
Defined the gesture classes and data requirements. Collected and tested examples for open palm, thumbs-up, neutral/no gesture, multiple users, different angles, lighting conditions, distances, and gloves.

3:00–4:00 PM — Model Development & Testing
Prepared the dataset and began training/testing the gesture-recognition model. Evaluated recognition behavior and discussed temporal movement detection for Swipe rather than relying only on a static image class.

4:00–5:00 PM — Deployment & Camera Integration
Worked on deploying the software to the Arduino UNO Q. Investigated camera access and the Linux/SSH workflow for running the vision pipeline on the board.

5:00–6:00 PM — System Integration & Debugging
Integrated the AI/software and hardware components, tested the end-to-end workflow, identified remaining camera/deployment issues, and refined the system for the final demonstration.