AUG - DEC 2016
Background: Georgia Tech's uses a source separation method to collect recycling. Multiple bins, each for a different type of waste, are grouped together. When throwing something away, it is up to the individual to dispose of their waste into the correct container.
Problem: How can we improve the quality of recycling collected on campus?
Solution: reco is a smartwatch app that helps users quickly determine whether or not something is recyclable. The system intervenes right as you are about to throw something away and guides you to the correct bin. By interacting with reco, users will learn through experience about how to properly sort waste.
I led the team of four in our initial discovery research and in gathering design feedback. My responsibilities included determining the best methods/metrics and creating a specific and actionable research plan.
I contributed to ideation, interaction and visual design, and prototyping. Through multiple iterations, mockups and prototypes helped us evaluate and improve our design.
As someone who cares about the environment, I wanted this project to have true potential benefits. I took initiative in working with the Campus Recycling Coordinator to make sure our design was addressing a real need.
Goal: To explore attitudes and beliefs around recycling and sustainability
Participants: 14 total (10 students, 4 representatives of sustainability organizations on campus)
Procedure: Each participant took part in an individual semi-structured interview.
Goal: To learn about students' existing knowledge of proper waste sorting
Participants: 5 students
Procedure: Three different recycling centers around campus were tested. Participants completed a think-aloud protocol while sorting a sample of everyday waste items. They also rated their confidence in each decision and took part in a brief follow-up interview.
Goal: To examine the influence of real-world factors on waste sorting behavior
Participants: 25 students
Procedure: We observed four different locations around campus for how students actually dispose of their waste, considering the impact of time pressure, social context, physical position of bins, etc.
There are multiple factors that affect students' waste sorting behavior. These factors can be broadly categorized into two groups.
1. Knowledge & Feedback
The majority of students did not know all of the nuanced rules involved in source separated recycling, which often led to confusion and decision fatigue. Without feedback, users couldn't learn how to improve their waste sorting behavior. This was an issue that could not be addressed by simply redesigning the bins themselves.
"I'm not sure about all the kinds of materials that are recyclable and which are not - some plastics apparently aren't."
2. Convenience & Interia
Many students shared about the challenge of translating their intent into action. Factors such as forgetfulness, physical proximity, and social context played a significant role in shaping behavior.
"I definitely care a lot, but convenience gets in the way. Like with recycling, if it's easier for me to just toss it out I would just do that."
To synthesize our research and guide our designs, we created personas of our target user group. These personas characterized a typical user’s thoughts, feelings, words, and actions. We made sure to account for varying levels of experience with recycling, as well as Georgia Tech’s diverse student body.
Diverge → Converge
Each group member came up with +20 ideas. We then synthesized and evaluated ideas along 2 dimensions.
User-to-System scale (x-axis)
Ideas on the left (User) are more technologically simple, but require more effort from the user. Ideas on the right (System) require less user involvement, but require more technology processing.
Transtheoretical Model (y-axis)
We used this model of behavior change technologies. The model classifies 5 phases in which behavior change can occur (bottom to top): Precontemplation, Contemplation, Preparation, Action, and Maintenance.
Passive Lock Screen Training
Users unlock their phone by sorting a piece of virtual trash into a bin. Responses are tracked and data is presented to users so they can learn how to recycle properly
Expert Assistance Bin
Students place items into a designated bin when they are confused about whether or not it can be recycled. Experts remotely sort the item through a mobile app and notify the original owner.
AR Education Game
Students participate in a campus-wide multiplayer game where they can "capture" territories. This is done by correctly sorting virtual trash into physical bins around campus.
Proximity sensors on bins will trigger a nudge on users' smartwatches when they are about to throw something away. The app then guides them to the proper bin to dispose of their trash.
We created low-fidelity paper prototypes for each of the 4 ideas. We then demoed these prototypes and received feedback through a dot-voting exercise.
Ultimately, we decided on the smartwatch app for the following reasons.
User Feedback: The concept was well-received by our target user group and other designers.
Value Added: The solution addresses both user needs identified from our discovery phase (knowledge and convenience)
Feasibility: Creating a high-fidelity prototype of a smartwatch app fit best within our time and resource constraints.
Overall, this idea was a simple, elegant, and supported by our research on technology-based behavior change.
Storyboard and User Flow
reco works by communicating with sensors attached to recycling centers around campus. As you approach a recycling center, a sensor will trigger when you come within a certain distance of a bin.
Each type of bin (Plastic, Landfill, Aluminum, Paper) has it’s own unique proximity sensor. This proximity sensor will send a notification to your smartwatch.
The initial notification is designed to catch your attention and make you think twice before throwing something away. To follow up, a short series of simple yes/no questions is used to guide you to the right place to dispose of your trash.
UI and Interactions
We had several whiteboarding sessions to determine how users should interact with the app, given physical, environmental, and other practice constraints.
To facilitate ease-of-use and remove distractions, the UI was designed to look simple and minimalist. We designed basic interactions that can be quickly executed while on-the-go.
Users are guided to the correct bin by answering a series of yes/no questions about the item they are holding.
For the decision tree and question wording, we focused on the following criteria:
- Reduce length of questions to facilitate readability and comprehension
- Minimize number of questions to encourage quick interactions
- Robust filtering, for a system that can sort the majority of common waste items
Based on our designs, there were two main areas of development: the smartwatch app and the proximity sensors.
The app was developed in Java for the Moto 360, an Android Wear-based smartwatch.
proximity sensors (wizard of oz)
The proximity sensors were originally envisioned to be developed with Arduino and installed on the containers to trigger the notification. However, due to project time constraints, we decided to mimic the sensor effect with a Wizard of Oz prototype. We created an auxiliary wizard dashboard that could manually trigger notifications on the smartwatch.
To cap it all off, we created a poster and video to present our work.
- I learned a lot over the course of this project, especially about the importance of discovery research. Exploring the problem space and identifying pain points were foundational steps in overall design process.
- In the future, further user testing could be done to evaluate the usability and efficacy of reco. We hypothesize that reco users will show measurable improvements in waste sorting accuracy and knowledge gained.
- We owe a huge thanks to the Campus Recycling Coordinator for her support and feedback throughout this project!