Air Quality Egg

Air Quality Egg collects data from an external sensor and crowd-sources it for the community

During a 3 month period, my team studied an existing product, the Air Quality Egg, and conducted a case study on why the project failed. After a comprehensive study, we decided that creating an iOS application would be the best solution to the problem. 




From our research, we identified two primary issues. The first issue was low user interest. To increase interest in the Air Quality Egg, the current website might need to be updated, with not only advertisement of the product, but also detailed education about the health hazards of the gases that are being monitored by the egg, as well as advice in lowering their numbers.

The second issue was the general reliability and accuracy of the egg. If the egg is unreliable and/or inaccurate, then its reason for being is called into question, and user interest will quickly wane. For this issue we mainly came up with goals rather than true solutions, which included easier updating, increased shell durability, smaller size, stronger sensors, and higher data security.




We chose a cognitive mapping diagram to assist us in brainstorming our main problem, which is that the Air Quality Egg is difficult to use. We believe that the cognitive mapping diagram was the best choice because it is extremely flexible and dynamic, both of which we found useful for our brainstorming session. Seeing the connections between the egg’s characteristics, such as its build quality, reliability, and therefore ease-of-use, helped us understand the egg’s issues more clearly. 






Storyboarding allowed us to understand the different use cases of the Air Quality Egg. This lead to an detailed understanding of what the user wanted and tailor our solution to fulfill their needs.