An app and a wearable ecosystem that helps pregnant people manage nausea
using machine learning
Lia helps reduce the impact of nausea on a pregnant person's lifestyle by helping them understand nausea, respond to it and receive support from loved ones
A 9-week grad capstone project advised by Artefact
Ideation, Concept Development, Wearable Design & Prototyping, Information Architecture, Wire-framing, Layout Design, Interface Design, Product Experience, Systems Design & Project Management
In the United States, around 5 million people experience nausea during pregnancy every year. Nausea is attributed to rising hormone levels and is most prevalent during the first trimester, but for some, it can last until the end of pregnancy.
Although it affects a significant number of people, nausea is overlooked by doctors as it is accepted as a normal part of pregnancy.
Nausea can significantly reduce the quality of life, increasing feelings of depression, and negatively impacting employment, household responsibilities, parenting, and family relationships.
Understanding nausea and finding suitable remedies is key to reducing its impact, but it is often a difficult and unreliable process.
This is where Lia comes in.
Lia is a machine learning-driven design concept that helps pregnant people manage nausea. It consists of a wearable that detects nausea and an app that surfaces the nausea pattern and recommends tailored context-specific remedies.
From the research phase of this project arose three key insights that guided the design of Lia. They are as follows:
The lack of a fine-tuned understanding
of nausea patterns makes it difficult to prepare for ahead of time.
Varied patterns of nausea between people cause them to seek solutions through trial-and-error, which can be time-consuming or lead to defeat
The mental toll on pregnant people is heightened by gaps in communication and support during the first trimester.
While research participants had a general idea of their nausea,
they expressed that it still felt unpredictable and opaque. I saw an opportunity to help pregnant people better understand and plan for their nausea.
For many of the participants, nausea felt like an insurmountable barrier that they resigned themselves to. I wasn't seeking to solve nausea altogether, rather I saw an opportunity to improve the process of discovering remedies in a personalized way.
Since nausea in most cases occurs before people announce their pregnancy, pregnant people rely heavily on a small circle of people for support. But at the same time, it can be difficult to communicate the support they need with their loved ones. I wanted the design response to include loved ones, to reduce the burden of nausea on the pregnant person.
Assist with understanding
& planning for nausea
Personalize the process of discovering remedies
Reduce the burden on
the pregnant person
Additionally, through the course of this project, I hoped to explore three specific interests through design. They are as follows:
Using data to empower human beings
Making data intuitive and actionable
Promoting human agency
To begin exploring these opportunity spaces
I led the ideation through a braiding session. The goal was to come up with as many ideas and directions as possible. During this process, I tried to explore a mix of physical and digital modes for the solution.
Ideation Sketches [Click to enlarge]
By tying these ideas to the research insights,
I identified 3 overlapping directions that also incorporated my design interests in data.
Integrating them together would create a more holistic system.
Design Directions [Click to enlarge]
To transform these high-level ideas into what would become Lia, I set out to answer 3 overarching questions:
How can I make Lia possible?
How can I make Lia ethical and inclusive?
How can I make Lia easy to understand and act upon?
Making Lia Possible
- using pattern matching & ML to understand nausea
- what data is needed?
- collecting the data (ideation, designing the wearable)
Making Lia Inclusive & Ethical
- easy opt-out system
- privacy & sharing
- anonymization of data
- design of the case
Making Lia Actionable
Lifecycle of Lia (Inclusive, Accessible & Sustainable)
- inclusive language
- distribution methods (?)
- overall lifecycle
Interactions for Scale
- Thinking of other stakeholders
- What can Lia grow into?