Calypso Smart Mask
BIOMETRIC SENSING AND RESPONSIVE FACE MASK FOR LONG-TERM COMFORT.
The Role
Product Manager, Fabrications
The Problem
Wearing a face mask is highly uncomfortable. Although the need for a mask requirement over the course of the COVID-19 pandemic was lifted, for individuals affected by a health issue, the need for a mask is a daily necessity.
How can we increase comfort in general mask wear?
With our problem statement in mind, my team and I first identified the need to define what users meant by “comfort” and “discomfort.” Through a series of surveys, observations, and literary research, we identified the feeling of breathlessness as the main factor of discomfort.
The feeling of breathlessness was linked with increased temperature, with 80% of participants finding the most discomfort when engaging in physical activity and over 60% finding discomfort on warmer days. The findings from our initial user research laid the groundwork for our minimally viable product (MVP).
The MVP
To combat breathlessness, the face mask needed structure and the ability to regulate internal mask temperature while maintaining the mask’s protection.
We created and tested the initial prototype using surveys, within-subject testing, and A/B testing to further define the user requirements and product specifications.
Challenges
70% of the way into development, our team came to a fork in the road: improve the form factor or expand on the the app that makes mask data available to the user.
As product manager, I assessed the timeframe and feasibility of development goals for both paths, and whether or not the choices would meet user requirements. Ultimately, the app would only deliver data that could not be converted into actionable changes.
The Solution
Users wanted a mask that felt comfortable to wear, one that was breathable and didn’t get too hot.
Through multiple prototype iterations and user testing, we arrived at a flexible mask structure that utilized a fan to regulate mask temperature. The mask was able to detect increased activity and automatically adjust mask temperature through machine learning.
Takeaways
At the conclusion of the project, I gained multiple takeaways:
Significant experience leading a remote, international team. Meeting formats and team expectations had to be adjusted to be effective.
Need for multiple user testing methods simultaneously to support results and supplement each method’s shortcomings.
More data doesn’t make a product more meaningful. Data without context or actionability isn’t useful.