Projects

Björn Friedrichs

That's me

PDFBlaze – pdfblaze.com A free templated PDF generation tool. Use the no-code editor to create reusable PDF templates and insert data to generate PDFs. PDF rendering engine written from scratch in Rust.

Vechsel – On GitHub A macOS utility application written in Swift to switch application windows by replacing the standard ⌘+Tab behaviour.

Nintendo Switch - GBA Emulator – On GitHub Utilising the restricted Nintendo Switch browser to capture input and receive streamed emulator data from a remote Python server.

This website – On GitHub Astro website including multiple fun components such as the Code Animator for creating code snippet animations.

Infrared sound remote On GitHub – Problem: Speakers that connect to a TV with a proprietary remote control. Solution: A infrared transceiver and a RPi + a local web server.

And much more... There is a lot of testing, prodding and half-finished bits. Feel free to visit my GitHub profile to see them.

Fun

Code animation tool – Code animator – A tool to create animated code snippets for blog posts and presentations. Write multiple code snippets and the tool will create a smooth transition animation between them. Code Animator Preview

Virtual bookshelf – My bookshelf – A 3D rendered virtual bookshelf to showcase my physical book collection. Built with Three.js. I wrote an article about how this works in this blog post.

Wikipedia daily game – Play now – A game that challenges you to connect two random Wikipedia articles by navigating through links.

Research

Decoding User Behaviour from Smartphone Interaction Event Streams Open Access Link This is my PhD thesis, it compounds my individual research over the years into one document.

Discovering Types of Smartphone Usage Sessions from User-App Interactions View on IEEE Xplore In this paper, we examine how embedding physical user-app activity (e.g., taps and scrolls) can provide a rich basis for summarising device usage. Using a large dataset of 82,758,449 interaction events from 86 users over an 8-week period we combine feature embedding and unsupervised learning to extract prominent interactions within clusters of smartphone usage sessions.

Utilising the co-occurrence of user interface interactions as a risk indicator for smartphone addiction View on Elsevier The study highlights a novel methodology to transform and analyse large amounts of interaction events to infer a user's level of smartphone addiction. This is a step forward from using commonly used metrics such as pure screen on time which can misrepresent the cognitive complexities and dependencies of human behaviour.