Over the past three days, I’ve been immersed in a motion capture lab held across our cluster of research centers. What began as an exploration into experimental MIDI controllers has unexpectedly evolved into a deeper, more ambitious side project: designing a low-cost motion capture peripheral. Using Raspberry Pi microcontrollers and an assortment of sensor modules, I’m exploring accessible, frugal solutions for capturing motion data.
The seed for this project was planted on the first day of the lab during a series of creative and technical conversations with colleagues. Initially, my intent was to tinker with MIDI controllers and motion to trigger and manipulate sound, but our discussions veered toward traditional performance capture, leading me to refocus my attention on performance tracking using the same off-the-shelf components. Shout-out to Mark Coniglio of Isadora fame for his enthusiasm and support. Our conversations led to beginning of the idea.
I started working through some basic code to test the feasibility of the concept in between a bunch of other interesting lab projects and activities, but didn’t get much further than that, although that in itself is a good thing, as the level of activity showed the second iteration of the lab to be a resounding success. I have since continued working on the idea in my home studio on evenings and weekends.
What interests me most about this little hoembrew project is its foundation in accessibility and creativity. Using affordable and widely available components like Raspberry Pi Pico microcontrollers and presence sensors, I’ve been able to rapidly prototype a design that doesn’t just work but also aligns with the principles of frugal education and design. To make this possible I turned to artificial intelligence to help me build my idea.
My virtual collaborator.
As someone with limited prior experience in programming Raspberry Pi Pico microcontrollers, I faced a bit of a learning curve, but I was determined to use this challenge as an opportunity to expand my skills. Inspired by one of our recent GameChangers projects on the playful use of AI in education, I embraced the help of ChatGPT, my virtual tutor, collaborator, and coding assistant. Taking my own advice and leverage my available resources. 😉
ChatGPT transformed how I approached this project. Instead of staring at a blank screen or getting bogged down in coding syntax, I could dive straight into the fun part—breaking, editing, and building code. Failing, learning, and progressing much faster. With ChatGPT, I had a constant resource for troubleshooting, refining my understanding, and validating my ideas, no matter how rudimentary or off-track they might have been. This iterative, trial-and-error approach to coding resonated with my way of learning, making the entire experience engaging and productive, but also really really fun. It’s so empowering to know that no matter how ridiculous the question, I’m not going to be judged, just empathised with and guided with monk-like patience. I highly recommend giving it a try if you’re learning something new. What’s more, I’m doing this in chunks with the free version of ChatGPT, once I get enough ongoing projects to justify a pro GPT Plus account I’m sure this will get even more enjoyable with the new natural language voice chat features. I’m now hovering over the subscription button now I’ve written this all down…
Building the Prototype
Armed with sensors, microcontrollers, and guidance from my AI collaborator, I’ve begun constructing the prototype. The current minimum viable prototype involves using presence sensors connected to Raspberry Pi Pico microcontrollers sending data wirelessly to a yet-to-be-determined application. Though I’m still early in the prototyping phase, the possibilities feel endless. Whether or not the final device meets all my initial expectations, the journey has already been immensely rewarding. It has challenged me to think creatively, learn resourcefully, and embrace AI for learning and practice building. The ultimate goal is to interpret this motion data in a way that contributes to accurate, functional motion capture. I’ll explain more about this in follow-up posts.
A Case Study in Frugal Education and Design
This project serves as a compelling case study for frugal education practices. While we often associate frugality in education with cost-effective teaching strategies or resource reuse, this project highlights another dimension: frugal innovation in practice and research. By leveraging low-cost hardware and open educational resources like ChatGPT and Raspberry Pi’s own documentation, combined with second-hand equipment and existing knowledge, I’ve been able to explore creative ideas without requiring a significant financial investment.
I hope to share my learnings more formally, perhaps through a collaborative paper exploring the intersection of frugal design and practice-based research. This is my next course of action, starting these conversation before the soon approaching Christmas and New Year break.
This project reminds me that innovation doesn’t require deep pockets—just a willingness to learn, experiment, and collaborate. Stay tuned as I continue to build and refine this concept, one winter’s evening at a time.