sllow is a part of a diet health management system, working with a smart placemat to monitor and manage an important eating habit: eating rate. Our system is capable of detecting an elevated eating rate and communicating the need for correction. sllow uses a weight sensing placemat that communicates with an accompanying mobile application. The application provides real-time analysis of a person’s eating rate, informing the user when they are eating too fast or too slow for their specified goal. Initial observations indicate sllow can successfully modify a person’s eating rate.

This is a 4-month remote group project completed during COVID-19 pandemic with David Hoatlin, Ziwei Wang, Jel Kewcharoen, and Joshua McFarlin. This project has a both a hardware (a smart placemat) and a software deliverable (the companion app). This portfolio only showcases the UX design of the companion app.

#mHealth   #IoT   #Mobile Interface Design   #User Evaluation   


  • Led a cross-functional team. Host weekly stand-ups and report to project advisors.

  • Took full lead on conducting user research, developing the user flow, wireframes, and prototypes;

  • Co-led system design;

  • Assist with hardware design and 3D printing.

Why this project?

Higher eating rate is associated with increased energy intake, BMI, obesity, and metabolic syndrome across various studies involving participants of different ethnicity, sex, and health condition. While significant effort has been devoted to researching methods for detecting an individual’s eating rate, we have identified a gap in solutions on communicating the need for eating rate modification after an elevated rate is detected.

The system we have developed is capable of both real-time eating rate detection and long-term goal setting and analysis. sllow is unique because it combines various components of successful research into a fully realized system for both eating rate detection and behavior modification.

01   Problem Space.

Eating at a proper speed is essential to our health.
Fast eating rate is significantly associated with higher chance of multiple health risks, while eating too slow can also compromise mindful eating and lead to negative outcomes.

However,  little common understanding have been established on the importance of eating rate, and subjective estimation of eating rate is unreliable on an individual level.


Thus, our task is to imagine and design a technology that can help users establish a healthier eating rate. The intervention should be sustainable and easy-to-use.

The target users should be people who have awareness in healthy eating, but might not be fully informed about the risks and benefit associated with different eating speed.

02   Opportunity Discovery.

1   Research Methods.


In order to comply with social distancing rule, all researches have been conducted remotely. We primarily looked at literature related to eating rate detection and behavior intervention method. User research participants were recruited through Slack and Reddit in the Georgia Tech community.

Key Findings.

Current studies focus more on eating detection technologies rather than intervention methods, and the few existing intervention tools have poor user experience and lack supporting evidence of effectiveness from long-term studies. We found that users are interested in this idea, but are looking for a more effortless and enjoyable solution when using a product as such.

Semi-structured User Interviews

20 Questions, 60 min.
Recruited on Reddit.

Existing Systems Evaluated

Including commercial and academic projects.

ER Detection Methods Examined

Based on 27 research papers.
From ACM Digital Library.

2   Journey Map


We created a journey map to better understand the painpoints along the process of eating rate tracking. Because the lack of commercial products in this category, we identified necessary stages of establishing proper eating rate based on literature review and user interviews.


We noticed that users are having a difficult time properly evaluating their eating speed and derive actionable insights. User's self evaluation on eating speed is highly depending on their family members and friends, and lack an objective metrics to reach more reliable conclusion. Besides, users do not know how to adjust their eating speed on a daily basis and develop a habit.

Detailed Insights.

Preparation stage:

Users want to understand how it can help/harm their health goal.

Users do not have sufficient understanding on the importance of eating rate. Even for users who have a vague idea on the importance, they are not associating their health goal with their eating rates.

Collection & Integration stage:

Users want a reliable method to evaluate their eating rate.

The unreliability of subjective evaluation has shown both in literature review and our user research. In order to get un-biased result and suggestions, users need an objective monitor method.

Reflection stage:

Users want to analyze their eating rate and to know how they can improve.

In addition to having an objective tracking method, users need to be able to understand the data and get insights on how to improve their habits based on their goal.

Action stage:

Users want to make real-time/long-term adjustment on eating rate.

Most importantly, users need a solution that can motivate and remind behavior change, both on a daily basis and on the long run.


Our Design Should...

Catered to individual's heath goal, and provide personalized suggestions.

#added value

Require minimal effort after initial setup to encourage retenion.

#learning curve

Provide actionable insights to help build a healthier habit.


Provide more personalized options for notifications based on preference and progress.

#mindless computing

03   Ideation

1   Key Features.


Our design include a placemat and its companion app. We designed several features for the two components respectively, and decided on how the two components work together as a system.

Why these features?

Our interviews shows that our users prefer an un-intrusive way to show them real time updates on eating rates. However, preferences and needs for different features are quite different, so we provide customization for the following aspects:

  • Different users have different goals: losing weight, maintaining weight, or gaining weight.

  • Users prefer different types of visualization to get different actionable insights.

  • Users prefer notification of different frequency, intensity, and modality: some prefer looking at the app's screen while eating, others want more sutle push notification only when necessary.

2   User Flow.

3   Wireframe.

04   Concept Development

1   Design System.

2   Key Flow.

Flow 01

Flow 02

Flow 03

Flow 04

05   Reflections

For this project...

We have developed a physical and digital prototype that tackles an important but overlooked part of dietary health. Using the application, users are able to analyze their eating habits in real-time and alter their behavior to match their stated preferences for eating rates. Our initial results are promising, with users making modifications to their current eating habits, and a very high usability based on our user and expert evaluation.

However, Further evaluation is required to fully understand sllow’s effectiveness. Future studies should monitor how eating rates of Sllow users change over the duration of several months, and determine if there are any observable impacts on the participant’s overall health. 

In the future...

Design for Personal Health Informatics

It is important to consider the different stage of PHI: preparation, collection, integration, reflection, and action. In this project, we identified that users have a difficult time collecting objective data and reach un-biased, motivating conclusions, and thus designed our features accordingly. Also, it is worth noticing that users will have very different goals and preferences, and require customization of the basic system setup. These goals and preferences are in many cases fluid and subjective, thus it is a tricky question on when and how frequent should the users be remind to set up or update their settings.

Design for long-term using

Because users generally lose interest of health tracking apps over time, it is important to consider how to motivate users in the long run. To solve this outstanding issue, we identified two helping factors : easy hardware setup and mindless computing on software. The previous require a hardware to be easy to set up, charge, and clean, and the latter requires no additional effort to track behaviors after initial setup. Based on the positive feedback we collected in this project, we believe that these ideas can potentially be helpful for future similar projects

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