Kittygram: A Student IoT Project Helping Monitor Cat Health
10 min. | 20. 7. 2026
The student project Kittygram shows how IoT, data analysis and artificial intelligence can support everyday pet care. The smart scale placed under a cat litter box monitors toilet visits, detects unusual behaviour patterns and could help owners and veterinarians identify possible health problems earlier.
How would you simply introduce the project to someone hearing about it for the first time? What exactly does the smart scale under the cat litter box, Kittygram, monitor, and how can it help cat owners?
Kittygram is a smart device that turns an ordinary cat litter box into an intelligent monitoring centre. It works as an intelligent mat with a weight sensor. The device sends owners notifications about successful or unsuccessful visits to the litter box, then analyses these visits and can alert them to unusual behaviour. This helps owners detect potential health problems in their cat before they fully develop.
Why did you decide to focus specifically on cat health and visits to the litter box? Was it based on personal interest, a specific problem, or did the idea only come about as part of the IoT course?
This topic is very close to us as a team – most of us own cats ourselves, so we know the issue first-hand. We had been thinking about a similar concept even before the IoT course began, but the course gave us additional motivation to try it out.
The project was created while studying the Software Development programme. What did you want to try out technically?
We wanted to try out the complete development cycle from hardware to software. The development of a native mobile application and the specific connection and integration of the weight sensor were key for us. From a developer’s point of view, the challenge was to create a reliable data pipeline for collecting, cleaning noise from, and subsequently processing raw data on the backend, as well as interpreting the results.
How difficult is it in a similar IoT project to combine hardware, software, data, and practical use in a household?
At the moment, we are struggling the most with hardware universality. We are trying to design the mat so that it is mechanically compatible with any type of cat litter box. The second major challenge is optimising network communication with the server so that we can keep energy consumption to an absolute minimum.
You are currently partially restarting the project after the workshop. What did the first version show you, and what were its limitations?
You are also working on improving data analysis and prediction using AI. In what way do you think AI could help the project the most?
We plan to use artificial intelligence for households with multiple cats, where it will help us identify a specific pet based only on its behavioural profile and weight data. At the same time, when monitoring anomalies and deviations in the data, we plan to use algorithms for detecting differences and map them to our specific problem. A challenge could then be interpreting the results of these algorithms.
Note: This is an illustrative image generated using AI.
On the project website, you want to present the device and collect feedback through forms. What information or reactions from cat owners would help you the most in further development?
We have also prepared a separate questionnaire for veterinarians, in which we ask them about this issue and how they perceive it as a whole. We would like to cooperate with veterinarians so that our product is as good as possible and provides the most accurate interpretation of the measured data.
What has working on the project given you from the perspective of studying Software Development? How does working on a school assignment differ from working on a project that could one day have real users?
Where would you like to take the project next if you manage to verify its functionality, collect data, and fine-tune the analysis?
During the IoT course and the workshop, six of us worked on the device. After completing the course, however, due to time constraints and the desire to work on the project truly actively, we agreed to reduce the team to three members. In this setup, we are able to work more efficiently and move the development forward much faster.
Our roles are clearly divided: David Sobíšek is responsible for the backend and the IoT part, Michal Mrkva develops the mobile application and works on the cat health analysis, and Vratislav Beran works on the backend and the administration of the analysis algorithms.
The team for the IoT workshop also included: Marek Šefců (Backend), David Pešek (Frontend + analytics), Alina Rylova (Frontend + analytics)
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