This project presents the design and evaluation of PizzaBox, a 3D-printed, interactive food ordering system that aims to differ from conventional food ordering interfaces and provide an entertaining and unique experience when ordering a pizza. The system incorporates underlying technologies that support ubiquitous computing, including Arduino-based hardware and REST API connectivity to real-time services. PizzaBox underwent both low and medium fidelity testing while working collaboratively with participants to co-design and refine a product that is approachable to all age groups while maintaining a simple process for ordering food from start to finish. Final testing was conducted at an independent pizzeria where interviews with participants led to four discussion themes: usability and end-user engagement, towards connected real-time products and services, healthy eating, and evolution of food ordering systems.
Interviews show that PizzaBox would have greater appeal to a younger audience by providing a fantasy of helping in the creation and baking of the pizza, but also has a novelty value that all ages would enjoy. The playful, hands-on nature of the physical interface engages children in particular, turning the ordering process into an interactive experience rather than a purely transactional one.
The study further investigates the effect that PizzaBox has in encouraging new healthy habits or promoting a healthier lifestyle. By designing the interface to foreground ingredient choices and nutritional information, the project explores how the design can be improved to better encourage positive lifestyle changes through connected food ordering systems.
Arduino-driven pizza box that uses an RFID reader and WiFi to gather ingredient selections from tagged objects. Scans tags, displays messages on an LCD, and posts data to a PHP script that emails the order. Includes required libraries for WiFi, RFID, and storage handling.
Interactive pizza-ordering kit that teaches healthy choices. Hardware uses RFID tags read via Arduino to track toppings. A Flask web app on Raspberry Pi shows personalised nutrition data and Matplotlib charts from CSV data. Includes Python scripts, Arduino examples, and HTML templates.