Kivo is an AR app that helps users better understand bikes and simultaneously teaches techniques to enhance the overall riding experience. The development of this app involved collaboration with three other designers for a course called Advanced Interactions at York University's Design Program.
problem
Maddy identified several current barriers to this process, including gatekeeping within the cycling industry, complex codes and nomenclature for bike parts that complicate simple repairs, a deficiency in well-designed resources for clientele, and challenges in keeping professionals and clients updated on current modifications. Additionally, there's a need to organize a comprehensive database of bike parts and tools.
KIVO App Frames
solution
To address this problem we decided to design an AR App that visually teaches users about their bike and provides recommendations and descriptions on maintenance.
this is how we made it *
We wanted to enhance the overall bike maintenance experience for all types of bikers and use cases, including off-roading, tricks, and transportation. This improvement can extend to modding and customizing bikes for advanced cyclists. Additionally, focus on identifying errors and preventing tool misuse during the fixing processes
Summary of our findings
user research & findings
We decided to use the “Autoethnography” method to do our research. This process informed our design considerations for typical bike riding experiences. Utilizing this method, we thoroughly documented and considered both Maddy's anecdotal experiences and the sentiments of casual or beginner cyclists.
Our group, consisting of novice and casual cyclists, engaged in this process to gather insights. Notably, our autoethnography research focused on 4 feminine-presenting individuals under 5’5”, revealing discomfort during and after rides. While this demographic is a minority, it offers valuable representation of casual cycling experiences.
Our group, consisting of novice and casual cyclists, engaged in this process to gather insights. Notably, our autoethnography research focused on 4 feminine-presenting individuals under 5’5”, revealing discomfort during and after rides. While this demographic is a minority, it offers valuable representation of casual cycling experiences.
data organizing
We streamlined our data into an affinity diagram to categorize and summarize the general findings from the questionnaire, and then began breaking it down into personas.
System mapping
After the initial diagramming process we finalized and created a system map first in order to figure out the organizational structure of the app. This allowed us to then identify 6 key task flows to base our wireframes upon.
System map structure
Wireframing and diagramming
The next step we took was making use of different diagramming methods to begin assembling ideas to consider how a user will interact with an AR app. This process included mental models, storyboarding as well as ergonomic diagramming. Through working through each diagramming exercise, this allowed us to slowly refine our user flows.
Ergonomic diagraming
Journey mapping
final app showcase *
Developer Hand off and BrandingTakeaways *
As we progress in developing this app to enhance accessibility for users with varying skill levels, we've concentrated on addressing the challenge of displaying complex information. Our strategies include incorporating contrast checkers, designing modules with a balanced mix of essential information and simplicity, and limiting AR use to scanning and static elements. We plan to refine content through extensive user testing, explore third-party app connections like Strava, and expand the app's utility for bike service shops. Additionally, we're considering features tailored to advanced users, envisioning capabilities for visualizing custom bike builds and recording ride data to meet the needs of experienced cyclists.
Kivo App in use & app components
.webp)