SnackBoxA snack box that closes when you eat too much.

SnackBox is a behavior design project disguised as an IoT device. Inspired by watching kids who can't stop snacking once they start, the system physically restricts access to snacks based on calorie data. A balloon inside the box inflates as you eat — gradually closing the opening. Exercise more, and the balloon deflates, reopening access. It's not an app telling you to stop; it's a box that won't let you reach in. The system connects a mobile app (barcode scanning + user profile), Google Sheets (cloud data layer), AI (calorie limit + balloon ratio calculation), and physical hardware (gesture sensor + air pump + balloon) into one closed-loop interaction.

SnackBox is not a consumer product — it's a concept prototype exploring how physical computing can shape health behavior. The core idea: instead of notifying users to eat less, physically limit access based on real calorie data. The system treats the snack box as both an input device (detecting hand gestures) and an output device (inflating a balloon to restrict access) — creating a tangible feedback loop between eating and exercising.

The system has four layers. (1) User Input: a mobile app where users enter height, weight, and scan snack barcodes to retrieve calorie data via API. (2) Cloud Data: Google Sheets serves as a lightweight database, storing user profiles, snack calories, and consumption history. (3) AI Calculation: processes cloud data to compute three values — daily snack calorie limit, estimated calories per handful, and balloon inflation ratio. (4) Physical Device: an APDS9960 gesture sensor detects hand insertion, a microcontroller reads cloud data and controls an air pump that inflates/deflates a balloon at the box opening — physically restricting snack access based on calorie balance.

• Physical Behavior Intervention — doesn't just track calories; physically restricts snack access through a tangible balloon mechanism
• Closed-Loop System — eat more → box closes; exercise more → box opens — creating a direct feedback loop between behavior and consequence
• HCI + Physical Computing — bridges interaction design with embedded systems, treating a snack box as both sensor and actuator

• APDS9960 gesture sensor — hand insertion detection
• Air pump + balloon mechanism — physical access control
• Arduino / Particle Photon — sensor processing & actuator control
• Mobile app — barcode scanning + calorie API lookup
• Google Sheets API — cloud data storage & exchange
• AI calorie calculation — daily limit, per-handful estimate, balloon ratio

• Weekly AI Report — Planned feature for weekly behavioral analysis and healthier snack recommendations was not fully implemented due to Google Sheets API and microcontroller integration challenges.
• Prototype Stage — Hardware form factor is functional but not refined — the balloon mechanism and sensor placement need further industrial design iteration for real-world use.

• Physical interventions can be more effective than digital notifications for behavior change — tangible consequences create stronger feedback loops
• Google Sheets as a lightweight cloud database enables rapid prototyping but hits limits with real-time microcontroller communication
• Combining gesture sensing with actuator control in a single device requires careful timing and state management
• Behavior design projects need to balance playfulness with functionality — the balloon concept is engaging precisely because it's physical and slightly absurd

• Physical Computing
• Behavior Design
• IoT Prototype
• Cloud Data Pipeline
• AI Calculation