Cover photo for Coffee Learning. 3D model of an organics and recycling bin with a coffee cup and a webcam

Coffee Learning

Overview

Coffee Learning is a project that targets the incorrect recycling habits of many coffee drinkers, and how we can automate and simplify the process of recycling commercial coffee cups through experiential design and machine learning

ROLE
Ideation
UX Designer
3D Model designer
TOOLS
Arduino / P5.js / Google's Teachable Machine
Cinema 4D
3D Printers
Adobe Illustrator
TEAM
Daniel Churchill
Lauren Black
Samantha Landry
DURATION
1 Month (Nov - Dec  2019)

PROBLEM

Over 2 billion cups of coffee are consumed everyday, worldwide. Among this figure, are college and university students. Investigations into recycling habits at Sheridan college have revealed that a majority of the student body does not dispose of coffee cups correctly, even after Sheridan College implemented recycling measures across campus to better dispose of these cups in the appropriate methods.

CHALLENGE

How might we automate the recycling process to encourage recycling habits at Sheridan College and simplify the process overall?

SOLUTION

This smart recycling bin analyzes the coffee cup components, being the cup, the sleeve, and the lid, and decides for the user, which bin it belongs in. The user places each component one by one on the middle platform, and the camera detects which bin to turn the platform towards.

DESIGN PROCESS

EMPATHIZE

Research

Ideation for this problem began with primary research on what recycling solutions existed both inside of Sheridan College, and what is being used in large corporations today. Sheridan college has an organic recycling process in place to properly dispose of coffee cups (removing the plastic from the paper cups). However, this process is inhibited by the improper sorting that majority of students partake in. Sheridan encourages students to place the cups into the organics bin, and to not place the entirety of the cup, sleeve and lid in the garbage or recycling bins.

Technology

Through research, the team discovered a few different methods of recycling automation.

DEFINE

Users

The target audience for this project is Young adults, aged 17 to 25, attending Sheridan College

Persona

Pain Points

  • Users hate wasting time figuring out the correct choice
  • Many users simply do not care enough to spend the time to learn the correct answer
  • Users hate the ambiguity of the right choice being different depending on the location
  • Users often report misinformation being spread on campus

IDEATION

Product Brainstorming

After understanding the needs and pain points of Sheridan students, the team began brainstorming, researching, and sketching functional product designs. These designs needed to save the students time, and make it undoubtably clear what choice was correct and remove any necessary problem solving

Product Design

Initial Sketches
Before deciding to incorporate machine learning, the team experimented with low fidelity prototypes and sketches. These involved custom lids with holes shaped like the desired coffee cup part, circular lids that would rotate based on detected parts, and a screen that would provided colour feedback (Red/Green) based on the users attempt/ anticipated action to further UX experimentation.

PROTOTYPE

Testing

Majority of the time spent on this project was spent learning, teaching, and tweaking Google's Teachable Machine. The system needed to be taught what the different components that made up a coffee cup actually were.

The final concept identified 5 states that needed to be recognized by the system. These would include the Fully Assembled Coffee Cup, Cup, Sleeve, Lid, and Place Holder (Empty). Placeholder was necessary to return the system to a default and not in use state. The design also incorporated user feedback in the form of a red light when the Coffee Cup was Fully Assembled, and a green light following the automated movement, when a single component was placed on the platform.

Final Design

Outcome