Interactive high-fidelity prototypes were presented as solutions to students at the University of Washington for improving trash segregation behaviors
As one of the two designers on the team, I collaborated closely with my teammate to create all the visual assets. I prototyped and iterated on the main user flow and interface more than a dozen times based on feedback from users and internal meetings.
As this case study will later explain, there were a few directions that the project could have taken; the concept you're reading now is the result of our eventual decision, which I played a significant role in.
This project studies the garbage disposal and segregation practices of students at UW and arrives at a design solution to reduce the cognitive load on the users while segregating trash.
-p-2600.png)
To focus on our target audience—college students who are often in a rush and cause unintentional incorrect segregation, we decided to prioritize the issue of trash segregation on campus.
.png)
As one of the primary user types, Nihal represents the huge international student body at the Univeristy of Washington -- while being unfamiliar with the city's segregation rules, they are willing to learn and adapt.
.png)
As the other primary user, Robin represents the Seattle natives who are enviornmentally awared. However, while this group of people are conscious of their segregation behaviors, they are still overwhlemed by how fast the rules are updated and therefore unable to keep up.
A student faces many decisions when trying to segregate in front of a trash bin -- the mental burden usually leads to incorrect segregation.
Trash segregation rules usually vary from city to city, state to state, country to country, which cuases unintentional incorrect segregation.
The only way to be educated on segregation is through internet or flyers near bin; neither method is efficient for staying up-to-date.
.png)
A voice assistant for trash can that helps users to properly dispose of waste by providing voice guidance and feedback
A Scaning app that uses computer vision and machine learning to identify and sort different types of waste
A checklist app that automatically records student's on-campus purchases and provides visual guidelines for segergating the bought items
Iwas chosen because it provides users with the segregation information instantly and puts users at the center of the interaction while requiring very minimal effort to interact with. As a solution, it's also comparatively easy to implement technically.
The above diargam represents a complete user flow for the use case of using our product to segregate.
The above diargam represents a complete user flow for the use case of using our product to segregate.
The above diargam represents a complete user flow for the use case of using our product to segregate.
The above diargam represents a complete user flow for the use case of using our product to segregate.
In these two scenarios, neither the 'item' nor 'list' view is very helpful -- a new way to find items is needed.
Users now can ask about how to segregate any trash or ask to have customized grouping of trash.
A smart trash management service that automatically loads the items you bought with you student ID into the system and provides visual guidelines for correct segregation
With Bin, students have access to up-to-date segregation information on their phone without having to add the items themselves. The visual guides makes correct segregation very easy, alleviating user's mental burden.
Every item that is purchased via one’s Husky’s card (a student ID card offered to UW students) is automatically added to the homepage.
Users get step-by-step instructions on how to segregate the item they have recently purchased. Every time a user learned and trashed a purchased item, they will be given positive feedback and the environmental impacts.
With the tablet version of the app, segregation experts can easily label which bin each part of an item would go.
We tested our user's knowledge of trash segregation using the same survey twice -- before and after they used our product.
Based on comparison between user's knowledge of trash segregation before and after using Trasure, we can proudly say that our product has shortened the time people spend segregating by 65% and improved the accuracy of segregation by 63%.